From 26d44d524c19f6e0a967c90e4d62c31363ab763b Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Mon, 23 Feb 2026 22:06:46 +0200
Subject: [PATCH 01/27] added uv.lock and pyproject.toml

---
 pyproject.toml |   7 ++
 uv.lock        | 198 +++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 205 insertions(+)
 create mode 100644 uv.lock

diff --git a/pyproject.toml b/pyproject.toml
index efcba21..7fca336 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,13 @@ dynamic = ["version"]
 license = {file = "LICENSE"}
 name = "openptv-python"
 readme = "README.md"
+dependencies = [
+    "numba>=0.64.0",
+    "numpy>=2.4.2",
+    "pytest>=9.0.2",
+    "pyyaml>=6.0.3",
+    "scipy>=1.17.1",
+]
 
 [tool.coverage.run]
 branch = true
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..1626878
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,198 @@
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From 370931ffe03e7b2ccb7fa7e9228e825f6976c739 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Mon, 23 Feb 2026 22:10:16 +0200
Subject: [PATCH 02/27] added pre-commit

---
 pyproject.toml |  1 +
 uv.lock        | 86 ++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 87 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 7fca336..c53e208 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,6 +21,7 @@ readme = "README.md"
 dependencies = [
     "numba>=0.64.0",
     "numpy>=2.4.2",
+    "pre-commit>=4.5.1",
     "pytest>=9.0.2",
     "pyyaml>=6.0.3",
     "scipy>=1.17.1",
diff --git a/uv.lock b/uv.lock
index 1626878..a3e1270 100644
--- a/uv.lock
+++ b/uv.lock
@@ -2,6 +2,15 @@ version = 1
 revision = 3
 requires-python = ">=3.14"
 
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+version = "3.5.0"
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 name = "colorama"
 version = "0.4.6"
@@ -11,6 +20,33 @@ wheels = [
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 ]
 
+[[package]]
+name = "distlib"
+version = "0.4.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/96/8e/709914eb2b5749865801041647dc7f4e6d00b549cfe88b65ca192995f07c/distlib-0.4.0.tar.gz", hash = "sha256:feec40075be03a04501a973d81f633735b4b69f98b05450592310c0f401a4e0d", size = 614605, upload-time = "2025-07-17T16:52:00.465Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/33/6b/e0547afaf41bf2c42e52430072fa5658766e3d65bd4b03a563d1b6336f57/distlib-0.4.0-py2.py3-none-any.whl", hash = "sha256:9659f7d87e46584a30b5780e43ac7a2143098441670ff0a49d5f9034c54a6c16", size = 469047, upload-time = "2025-07-17T16:51:58.613Z" },
+]
+
+[[package]]
+name = "filelock"
+version = "3.24.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/73/92/a8e2479937ff39185d20dd6a851c1a63e55849e447a55e798cc2e1f49c65/filelock-3.24.3.tar.gz", hash = "sha256:011a5644dc937c22699943ebbfc46e969cdde3e171470a6e40b9533e5a72affa", size = 37935, upload-time = "2026-02-19T00:48:20.543Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/9c/0f/5d0c71a1aefeb08efff26272149e07ab922b64f46c63363756224bd6872e/filelock-3.24.3-py3-none-any.whl", hash = "sha256:426e9a4660391f7f8a810d71b0555bce9008b0a1cc342ab1f6947d37639e002d", size = 24331, upload-time = "2026-02-19T00:48:18.465Z" },
+]
+
+[[package]]
+name = "identify"
+version = "2.6.16"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5b/8d/e8b97e6bd3fb6fb271346f7981362f1e04d6a7463abd0de79e1fda17c067/identify-2.6.16.tar.gz", hash = "sha256:846857203b5511bbe94d5a352a48ef2359532bc8f6727b5544077a0dcfb24980", size = 99360, upload-time = "2026-01-12T18:58:58.201Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/b8/58/40fbbcefeda82364720eba5cf2270f98496bdfa19ea75b4cccae79c698e6/identify-2.6.16-py2.py3-none-any.whl", hash = "sha256:391ee4d77741d994189522896270b787aed8670389bfd60f326d677d64a6dfb0", size = 99202, upload-time = "2026-01-12T18:58:56.627Z" },
+]
+
 [[package]]
 name = "iniconfig"
 version = "2.3.0"
@@ -32,6 +68,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/30/a8/e61a8c2b3cc7a597073d9cde1fcbb567e9d827f1db30c93cf80422eac70d/llvmlite-0.46.0-cp314-cp314-win_amd64.whl", hash = "sha256:7821eda3ec1f18050f981819756631d60b6d7ab1a6cf806d9efefbe3f4082d61", size = 39153056, upload-time = "2025-12-08T18:15:33.938Z" },
 ]
 
+[[package]]
+name = "nodeenv"
+version = "1.10.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/24/bf/d1bda4f6168e0b2e9e5958945e01910052158313224ada5ce1fb2e1113b8/nodeenv-1.10.0.tar.gz", hash = "sha256:996c191ad80897d076bdfba80a41994c2b47c68e224c542b48feba42ba00f8bb", size = 55611, upload-time = "2025-12-20T14:08:54.006Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/b2/d0896bdcdc8d28a7fc5717c305f1a861c26e18c05047949fb371034d98bd/nodeenv-1.10.0-py2.py3-none-any.whl", hash = "sha256:5bb13e3eed2923615535339b3c620e76779af4cb4c6a90deccc9e36b274d3827", size = 23438, upload-time = "2025-12-20T14:08:52.782Z" },
+]
+
 [[package]]
 name = "numba"
 version = "0.64.0"
@@ -83,6 +128,7 @@ source = { editable = "." }
 dependencies = [
     { name = "numba" },
     { name = "numpy" },
+    { name = "pre-commit" },
     { name = "pytest" },
     { name = "pyyaml" },
     { name = "scipy" },
@@ -92,6 +138,7 @@ dependencies = [
 requires-dist = [
     { name = "numba", specifier = ">=0.64.0" },
     { name = "numpy", specifier = ">=2.4.2" },
+    { name = "pre-commit", specifier = ">=4.5.1" },
     { name = "pytest", specifier = ">=9.0.2" },
     { name = "pyyaml", specifier = ">=6.0.3" },
     { name = "scipy", specifier = ">=1.17.1" },
@@ -106,6 +153,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/b7/b9/c538f279a4e237a006a2c98387d081e9eb060d203d8ed34467cc0f0b9b53/packaging-26.0-py3-none-any.whl", hash = "sha256:b36f1fef9334a5588b4166f8bcd26a14e521f2b55e6b9de3aaa80d3ff7a37529", size = 74366, upload-time = "2026-01-21T20:50:37.788Z" },
 ]
 
+[[package]]
+name = "platformdirs"
+version = "4.9.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1b/04/fea538adf7dbbd6d186f551d595961e564a3b6715bdf276b477460858672/platformdirs-4.9.2.tar.gz", hash = "sha256:9a33809944b9db043ad67ca0db94b14bf452cc6aeaac46a88ea55b26e2e9d291", size = 28394, upload-time = "2026-02-16T03:56:10.574Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/48/31/05e764397056194206169869b50cf2fee4dbbbc71b344705b9c0d878d4d8/platformdirs-4.9.2-py3-none-any.whl", hash = "sha256:9170634f126f8efdae22fb58ae8a0eaa86f38365bc57897a6c4f781d1f5875bd", size = 21168, upload-time = "2026-02-16T03:56:08.891Z" },
+]
+
 [[package]]
 name = "pluggy"
 version = "1.6.0"
@@ -115,6 +171,22 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/54/20/4d324d65cc6d9205fabedc306948156824eb9f0ee1633355a8f7ec5c66bf/pluggy-1.6.0-py3-none-any.whl", hash = "sha256:e920276dd6813095e9377c0bc5566d94c932c33b27a3e3945d8389c374dd4746", size = 20538, upload-time = "2025-05-15T12:30:06.134Z" },
 ]
 
+[[package]]
+name = "pre-commit"
+version = "4.5.1"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "cfgv" },
+    { name = "identify" },
+    { name = "nodeenv" },
+    { name = "pyyaml" },
+    { name = "virtualenv" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/40/f1/6d86a29246dfd2e9b6237f0b5823717f60cad94d47ddc26afa916d21f525/pre_commit-4.5.1.tar.gz", hash = "sha256:eb545fcff725875197837263e977ea257a402056661f09dae08e4b149b030a61", size = 198232, upload-time = "2025-12-16T21:14:33.552Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/5d/19/fd3ef348460c80af7bb4669ea7926651d1f95c23ff2df18b9d24bab4f3fa/pre_commit-4.5.1-py2.py3-none-any.whl", hash = "sha256:3b3afd891e97337708c1674210f8eba659b52a38ea5f822ff142d10786221f77", size = 226437, upload-time = "2025-12-16T21:14:32.409Z" },
+]
+
 [[package]]
 name = "pygments"
 version = "2.19.2"
@@ -196,3 +268,17 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/11/2f/b29eafe4a3fbc3d6de9662b36e028d5f039e72d345e05c250e121a230dd4/scipy-1.17.1-cp314-cp314t-win_amd64.whl", hash = "sha256:eb092099205ef62cd1782b006658db09e2fed75bffcae7cc0d44052d8aa0f484", size = 37345327, upload-time = "2026-02-23T00:22:24.442Z" },
     { url = "https://files.pythonhosted.org/packages/07/39/338d9219c4e87f3e708f18857ecd24d22a0c3094752393319553096b98af/scipy-1.17.1-cp314-cp314t-win_arm64.whl", hash = "sha256:200e1050faffacc162be6a486a984a0497866ec54149a01270adc8a59b7c7d21", size = 25489165, upload-time = "2026-02-23T00:22:29.563Z" },
 ]
+
+[[package]]
+name = "virtualenv"
+version = "20.39.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "distlib" },
+    { name = "filelock" },
+    { name = "platformdirs" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/ed/54/809199edc537dbace273495ac0884d13df26436e910a5ed4d0ec0a69806b/virtualenv-20.39.0.tar.gz", hash = "sha256:a15f0cebd00d50074fd336a169d53422436a12dfe15149efec7072cfe817df8b", size = 5869141, upload-time = "2026-02-23T18:09:13.349Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/f7/b4/8268da45f26f4fe84f6eae80a6ca1485ffb490a926afecff75fc48f61979/virtualenv-20.39.0-py3-none-any.whl", hash = "sha256:44888bba3775990a152ea1f73f8e5f566d49f11bbd1de61d426fd7732770043e", size = 5839121, upload-time = "2026-02-23T18:09:11.173Z" },
+]

From 296432146d0143cbf460ba94bdc5adc76fc09676 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Mon, 23 Feb 2026 22:11:00 +0200
Subject: [PATCH 03/27] running make template-update

---
 .cruft.json                   |  4 +--
 .github/workflows/on-push.yml | 46 +++++++++++++++++------------------
 .pre-commit-config.yaml       | 14 +++++------
 Dockerfile                    |  2 +-
 README.md.rej                 | 14 +++++++++++
 pyproject.toml                |  1 +
 6 files changed, 48 insertions(+), 33 deletions(-)
 create mode 100644 README.md.rej

diff --git a/.cruft.json b/.cruft.json
index 6fb3fda..053d3c8 100644
--- a/.cruft.json
+++ b/.cruft.json
@@ -1,6 +1,6 @@
 {
   "template": "https://github.com/ecmwf-projects/cookiecutter-conda-package",
-  "commit": "a9c290fa0e810e9dee6c61991afac6f0c969c3b1",
+  "commit": "5ad0c955478c9b0fe8772545ef46291f5f314f75",
   "checkout": null,
   "context": {
     "cookiecutter": {
@@ -13,7 +13,7 @@
       "integration_tests": "True",
       "pypi": true,
       "_template": "https://github.com/ecmwf-projects/cookiecutter-conda-package",
-      "_commit": "a9c290fa0e810e9dee6c61991afac6f0c969c3b1"
+      "_commit": "5ad0c955478c9b0fe8772545ef46291f5f314f75"
     }
   },
   "directory": null
diff --git a/.github/workflows/on-push.yml b/.github/workflows/on-push.yml
index abacbb2..b04fbfb 100644
--- a/.github/workflows/on-push.yml
+++ b/.github/workflows/on-push.yml
@@ -22,8 +22,8 @@ jobs:
   pre-commit:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v4
-    - uses: actions/setup-python@v5
+    - uses: actions/checkout@v6
+    - uses: actions/setup-python@v6
       with:
         python-version: 3.x
     - uses: pre-commit/action@v3.0.1
@@ -32,8 +32,8 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
-    - uses: actions/setup-python@v5
+    - uses: actions/checkout@v6
+    - uses: actions/setup-python@v6
       with:
         python-version: 3.x
     - name: Install conda-merge
@@ -44,7 +44,7 @@ jobs:
         for SUFFIX in ci integration; do
         conda-merge ci/environment-$SUFFIX.yml environment.yml > ci/combined-environment-$SUFFIX.yml || exit
         done
-    - uses: actions/upload-artifact@v4
+    - uses: actions/upload-artifact@v6
       with:
         name: combined-environments
         path: ci/combined-environment-*.yml
@@ -55,11 +55,11 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.11']
+        python-version: ['3.11', '3.12']
 
     steps:
-    - uses: actions/checkout@v4
-    - uses: actions/download-artifact@v4
+    - uses: actions/checkout@v6
+    - uses: actions/download-artifact@v7
       with:
         name: combined-environments
         path: ci
@@ -87,8 +87,8 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
-    - uses: actions/download-artifact@v4
+    - uses: actions/checkout@v6
+    - uses: actions/download-artifact@v7
       with:
         name: combined-environments
         path: ci
@@ -103,7 +103,7 @@ jobs:
         cache-environment-key: environment-${{ steps.date.outputs.date }}
         cache-downloads-key: downloads-${{ steps.date.outputs.date }}
         create-args: >-
-          python=3.11
+          python=3.12
     - name: Install package
       run: |
         python -m pip install --no-deps -e .
@@ -116,8 +116,8 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
-    - uses: actions/download-artifact@v4
+    - uses: actions/checkout@v6
+    - uses: actions/download-artifact@v7
       with:
         name: combined-environments
         path: ci
@@ -132,7 +132,7 @@ jobs:
         cache-environment-key: environment-${{ steps.date.outputs.date }}
         cache-downloads-key: downloads-${{ steps.date.outputs.date }}
         create-args: >-
-          python=3.11
+          python=3.12
     - name: Install package
       run: |
         python -m pip install --no-deps -e .
@@ -149,12 +149,12 @@ jobs:
     strategy:
       matrix:
         include:
-        - python-version: '3.11'
+        - python-version: '3.12'
           extra: -integration
 
     steps:
-    - uses: actions/checkout@v4
-    - uses: actions/download-artifact@v4
+    - uses: actions/checkout@v6
+    - uses: actions/download-artifact@v7
       with:
         name: combined-environments
         path: ci
@@ -188,10 +188,10 @@ jobs:
       (needs.integration-tests.result == 'success' || needs.integration-tests.result == 'skipped')
 
     steps:
-    - uses: actions/checkout@v4
-    - uses: actions/setup-python@v5
+    - uses: actions/checkout@v6
+    - uses: actions/setup-python@v6
       with:
-        python-version: '3.11'
+        python-version: '3.12'
     - name: Install package
       run: |
         python -m pip install --upgrade pip
@@ -206,7 +206,7 @@ jobs:
         python -m twine check --strict * || exit
         python -c "import openptv_python" || exit
         cd ..
-    - uses: actions/upload-artifact@v4
+    - uses: actions/upload-artifact@v6
       with:
         name: distribution
         path: dist
@@ -226,10 +226,10 @@ jobs:
       id-token: write  # IMPORTANT: this permission is mandatory for trusted publish
 
     steps:
-    - uses: actions/download-artifact@v4
+    - uses: actions/download-artifact@v7
       with:
         name: distribution
         path: dist
-    - uses: pypa/gh-action-pypi-publish@v1.12.4
+    - uses: pypa/gh-action-pypi-publish@v1.13.0
       with:
         verbose: true
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 2922494..64c2474 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,6 @@
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v5.0.0
+  rev: v6.0.0
   hooks:
   - id: trailing-whitespace
   - id: end-of-file-fixer
@@ -12,28 +12,28 @@ repos:
   - id: debug-statements
   - id: mixed-line-ending
 - repo: https://github.com/keewis/blackdoc
-  rev: v0.3.9
+  rev: v0.4.6
   hooks:
   - id: blackdoc
-    additional_dependencies: [black==23.11.0]
+    additional_dependencies: [black==25.9.0]
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.11.11
+  rev: v0.15.2
   hooks:
   - id: ruff
     args: [--fix, --show-fixes]
   - id: ruff-format
 - repo: https://github.com/executablebooks/mdformat
-  rev: 0.7.22
+  rev: 1.0.0
   hooks:
   - id: mdformat
 - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
-  rev: v2.14.0
+  rev: v2.16.0
   hooks:
   - id: pretty-format-yaml
     args: [--autofix, --preserve-quotes]
   - id: pretty-format-toml
     args: [--autofix]
 - repo: https://github.com/gitleaks/gitleaks
-  rev: v8.26.0
+  rev: v8.30.0
   hooks:
   - id: gitleaks
diff --git a/Dockerfile b/Dockerfile
index 372b11c..2fbf6f2 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -4,7 +4,7 @@ WORKDIR /src/openptv-python
 
 COPY environment.yml /src/openptv-python/
 
-RUN conda install -c conda-forge gcc python=3.11 \
+RUN conda install -c conda-forge gcc python=3.12 \
     && conda env update -n base -f environment.yml
 
 COPY . /src/openptv-python
diff --git a/README.md.rej b/README.md.rej
new file mode 100644
index 0000000..31889ea
--- /dev/null
+++ b/README.md.rej
@@ -0,0 +1,14 @@
+diff a/README.md b/README.md	(rejected hunks)
+@@ -11,10 +11,10 @@ Python version of the OpenPTV library
+ 
+ ## Workflow for developers/contributors
+ 
+-For best experience create a new conda environment (e.g. DEVELOP) with Python 3.11:
++For best experience create a new conda environment (e.g. DEVELOP) with Python 3.12:
+ 
+ ```
+-conda create -n DEVELOP -c conda-forge python=3.11
++conda create -n DEVELOP -c conda-forge python=3.12
+ conda activate DEVELOP
+ ```
+ 
diff --git a/pyproject.toml b/pyproject.toml
index c53e208..33d2d18 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -11,6 +11,7 @@ classifiers = [
   "Programming Language :: Python",
   "Programming Language :: Python :: 3",
   "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
   "Topic :: Scientific/Engineering"
 ]
 description = "Python version of the OpenPTV library"

From a1bb0dffaaf870ccd882b77106baff3c092bac46 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Mon, 23 Feb 2026 22:23:58 +0200
Subject: [PATCH 04/27] everything works

---
 README.md      |   6 +-
 README.md.rej  |  14 --
 pyproject.toml |  23 ++-
 uv.lock        | 529 +++++++++++++++++++++++++++++++++++++++++++++++++
 4 files changed, 547 insertions(+), 25 deletions(-)
 delete mode 100644 README.md.rej

diff --git a/README.md b/README.md
index eb512ae..36d5595 100644
--- a/README.md
+++ b/README.md
@@ -19,16 +19,16 @@ this structure
 
 ### Workflow for developers/contributors
 
-For the best experience create a new conda environment (e.g. DEVELOP) with Python 3.10:
+For the best experience create a new conda environment (e.g. DEVELOP) with Python 3.12:
 
 ```
-conda create -n openptv-python -c conda-forge python=3.11
+conda create -n openptv-python -c conda-forge python=3.12
 conda activate openptv-python
 ```
 
 Before pushing to GitHub, run the following commands:
 
-1. Update conda environment: `make conda-env-update`
+1. Update conda environment: `make conda-env-update` or `uv venv` and `source .venv/bin/activate` followed by `uv sync`
 1. Install this package: `pip install -e .`
 1. Sync with the latest [template](https://github.com/ecmwf-projects/cookiecutter-conda-package) (optional): `make template-update`
 1. Run quality assurance checks: `make qa`
diff --git a/README.md.rej b/README.md.rej
deleted file mode 100644
index 31889ea..0000000
--- a/README.md.rej
+++ /dev/null
@@ -1,14 +0,0 @@
-diff a/README.md b/README.md	(rejected hunks)
-@@ -11,10 +11,10 @@ Python version of the OpenPTV library
- 
- ## Workflow for developers/contributors
- 
--For best experience create a new conda environment (e.g. DEVELOP) with Python 3.11:
-+For best experience create a new conda environment (e.g. DEVELOP) with Python 3.12:
- 
- ```
--conda create -n DEVELOP -c conda-forge python=3.11
-+conda create -n DEVELOP -c conda-forge python=3.12
- conda activate DEVELOP
- ```
- 
diff --git a/pyproject.toml b/pyproject.toml
index 33d2d18..f5946bd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,19 +14,26 @@ classifiers = [
   "Programming Language :: Python :: 3.12",
   "Topic :: Scientific/Engineering"
 ]
+dependencies = [
+  "mypy>=1.19.1",
+  "myst-parser>=5.0.0",
+  "numba>=0.64.0",
+  "numpy>=2.4.2",
+  "pre-commit>=4.5.1",
+  "pydata-sphinx-theme>=0.16.1",
+  "pytest>=9.0.2",
+  "pytest-cov>=7.0.0",
+  "pyyaml>=6.0.3",
+  "scipy>=1.17.1",
+  "sphinx>=9.1.0",
+  "sphinx-autoapi>=3.7.0",
+  "types-pyyaml>=6.0.12.20250915",
+]
 description = "Python version of the OpenPTV library"
 dynamic = ["version"]
 license = {file = "LICENSE"}
 name = "openptv-python"
 readme = "README.md"
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 branch = true
diff --git a/uv.lock b/uv.lock
index a3e1270..a4c40c8 100644
--- a/uv.lock
+++ b/uv.lock
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From 493f4bd03f94069cdf72d2976337b9a51b32b925 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Fri, 6 Mar 2026 17:54:44 +0200
Subject: [PATCH 05/27] working on bundle adjustment or volume self-calibration

---
 README.md                                     |   2 +-
 openptv_python/calibration_compare.py         | 150 +++++++
 openptv_python/orientation.py                 | 425 +++++++++++++++++-
 pyproject.toml                                |   2 +-
 tests/test_bundle_adjustment.py               | 204 +++++++++
 tests/test_calibration_compare.py             |  64 +++
 .../test_cavity/parameters_Run1.yaml          | 174 +++++++
 uv.lock                                       | 358 +++++++++++++--
 8 files changed, 1335 insertions(+), 44 deletions(-)
 create mode 100644 openptv_python/calibration_compare.py
 create mode 100644 tests/test_bundle_adjustment.py
 create mode 100644 tests/test_calibration_compare.py
 create mode 100644 tests/testing_fodder/test_cavity/parameters_Run1.yaml

diff --git a/README.md b/README.md
index 36d5595..1a873da 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@ conda activate openptv-python
 
 Before pushing to GitHub, run the following commands:
 
-1. Update conda environment: `make conda-env-update` or `uv venv` and `source .venv/bin/activate` followed by `uv sync`
+1. Update conda environment: `make conda-env-update` or `uv venv` and `source .venv/bin/activate` followed by `uv sync --upgrade`
 1. Install this package: `pip install -e .`
 1. Sync with the latest [template](https://github.com/ecmwf-projects/cookiecutter-conda-package) (optional): `make template-update`
 1. Run quality assurance checks: `make qa`
diff --git a/openptv_python/calibration_compare.py b/openptv_python/calibration_compare.py
new file mode 100644
index 0000000..28e79b0
--- /dev/null
+++ b/openptv_python/calibration_compare.py
@@ -0,0 +1,150 @@
+"""Utilities for comparing calibration folders camera by camera."""
+
+from __future__ import annotations
+
+import argparse
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Dict, Iterable
+
+import numpy as np
+
+from .calibration import Calibration, read_calibration
+
+
+@dataclass
+class CalibrationDelta:
+    """Numeric parameter deltas for one camera calibration pair."""
+
+    camera_key: str
+    position_delta: np.ndarray
+    angle_delta: np.ndarray
+    primary_point_delta: np.ndarray
+    glass_delta: np.ndarray
+    added_par_delta: np.ndarray
+
+
+def _camera_key_from_ori_path(ori_path: Path) -> str:
+    """Return the camera key shared by the .ori and .addpar file pair."""
+    if not ori_path.name.endswith(".ori"):
+        raise ValueError(f"Expected a .ori file, got {ori_path}")
+    return ori_path.name[: -len(".ori")]
+
+
+def _discover_calibration_pairs(folder: Path) -> Dict[str, tuple[Path, Path | None]]:
+    """Discover calibration files in a folder keyed by camera base name."""
+    if not folder.exists():
+        raise FileNotFoundError(folder)
+
+    pairs: Dict[str, tuple[Path, Path | None]] = {}
+    for ori_path in sorted(folder.glob("*.ori")):
+        key = _camera_key_from_ori_path(ori_path)
+        addpar_path = folder / f"{key}.addpar"
+        pairs[key] = (ori_path, addpar_path if addpar_path.exists() else None)
+
+    if not pairs:
+        raise ValueError(f"No calibration .ori files found in {folder}")
+
+    return pairs
+
+
+def _load_calibration_pair(pair: tuple[Path, Path | None]) -> Calibration:
+    """Load one calibration pair from disk."""
+    ori_path, addpar_path = pair
+    return read_calibration(ori_path, addpar_path)
+
+
+def compare_calibration_folders(
+    reference_dir: Path | str, candidate_dir: Path | str
+) -> Dict[str, CalibrationDelta]:
+    """Compare two calibration folders and return numeric deltas per camera."""
+    reference_dir = Path(reference_dir)
+    candidate_dir = Path(candidate_dir)
+
+    reference_pairs = _discover_calibration_pairs(reference_dir)
+    candidate_pairs = _discover_calibration_pairs(candidate_dir)
+
+    if set(reference_pairs) != set(candidate_pairs):
+        missing_from_candidate = sorted(set(reference_pairs) - set(candidate_pairs))
+        missing_from_reference = sorted(set(candidate_pairs) - set(reference_pairs))
+        raise ValueError(
+            "Calibration folders contain different camera sets: "
+            f"missing_from_candidate={missing_from_candidate}, "
+            f"missing_from_reference={missing_from_reference}"
+        )
+
+    deltas: Dict[str, CalibrationDelta] = {}
+    for camera_key in sorted(reference_pairs):
+        reference_cal = _load_calibration_pair(reference_pairs[camera_key])
+        candidate_cal = _load_calibration_pair(candidate_pairs[camera_key])
+
+        deltas[camera_key] = CalibrationDelta(
+            camera_key=camera_key,
+            position_delta=candidate_cal.get_pos() - reference_cal.get_pos(),
+            angle_delta=candidate_cal.get_angles() - reference_cal.get_angles(),
+            primary_point_delta=(
+                candidate_cal.get_primary_point() - reference_cal.get_primary_point()
+            ),
+            glass_delta=candidate_cal.glass_par - reference_cal.glass_par,
+            added_par_delta=candidate_cal.added_par - reference_cal.added_par,
+        )
+
+    return deltas
+
+
+def format_calibration_comparison(
+    deltas: Dict[str, CalibrationDelta],
+    reference_dir: Path | str | None = None,
+    candidate_dir: Path | str | None = None,
+) -> str:
+    """Format camera-by-camera calibration deltas as readable text."""
+    lines: list[str] = []
+    if reference_dir is not None and candidate_dir is not None:
+        lines.append(f"Reference: {Path(reference_dir)}")
+        lines.append(f"Candidate: {Path(candidate_dir)}")
+
+    for camera_key in sorted(deltas):
+        delta = deltas[camera_key]
+        lines.append(f"{camera_key}:")
+        lines.append(
+            "  position_delta: " + " ".join(f"{value:+.9f}" for value in delta.position_delta)
+        )
+        lines.append(
+            "  angle_delta: " + " ".join(f"{value:+.9f}" for value in delta.angle_delta)
+        )
+        lines.append(
+            "  primary_point_delta: "
+            + " ".join(f"{value:+.9f}" for value in delta.primary_point_delta)
+        )
+        lines.append(
+            "  glass_delta: " + " ".join(f"{value:+.9f}" for value in delta.glass_delta)
+        )
+        lines.append(
+            "  addpar_delta: " + " ".join(f"{value:+.9f}" for value in delta.added_par_delta)
+        )
+
+    return "\n".join(lines)
+
+
+def main(argv: Iterable[str] | None = None) -> int:
+    """Command-line entry point for comparing calibration folders."""
+    parser = argparse.ArgumentParser(
+        description="Compare two calibration folders camera by camera."
+    )
+    parser.add_argument("reference_dir", type=Path)
+    parser.add_argument("candidate_dir", type=Path)
+    args = parser.parse_args(list(argv) if argv is not None else None)
+
+    deltas = compare_calibration_folders(args.reference_dir, args.candidate_dir)
+    print(
+        format_calibration_comparison(
+            deltas,
+            reference_dir=args.reference_dir,
+            candidate_dir=args.candidate_dir,
+        )
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
\ No newline at end of file
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index 3b7b1a8..d92cb9b 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -1,6 +1,6 @@
 """Functions for the orientation of the camera."""
 
-from typing import List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import numpy as np
 import scipy
@@ -18,7 +18,7 @@
 from .ray_tracing import ray_tracing
 from .sortgrid import sortgrid
 from .tracking_frame_buf import Target
-from .trafo import correct_brown_affine, pixel_to_metric
+from .trafo import correct_brown_affine, dist_to_flat, metric_to_pixel, pixel_to_metric
 from .vec_utils import unit_vector, vec_norm, vec_set
 
 
@@ -1060,6 +1060,425 @@ def multi_cam_point_positions(
     rcm = np.empty(num_targets)
 
     for pt in range(num_targets):
-        rcm[pt], res = point_position(targets[pt], num_cams, mm_par, cals)
+        rcm[pt], res[pt] = point_position(targets[pt], num_cams, mm_par, cals)
 
     return res, rcm
+
+
+def _clone_calibration(cal: Calibration) -> Calibration:
+    """Create an isolated calibration copy for optimization updates."""
+    return Calibration(
+        ext_par=cal.ext_par.copy(),
+        int_par=cal.int_par.copy(),
+        glass_par=cal.glass_par.copy(),
+        added_par=cal.added_par.copy(),
+        mmlut=cal.mmlut,
+        mmlut_data=cal.mmlut_data,
+    )
+
+
+def _bundle_optional_parameter_names(orient_par: OrientPar) -> List[str]:
+    """Return optional calibration parameter names enabled for optimization."""
+    names = []
+    mapping = (
+        ("ccflag", "cc"),
+        ("xhflag", "xh"),
+        ("yhflag", "yh"),
+        ("k1flag", "k1"),
+        ("k2flag", "k2"),
+        ("k3flag", "k3"),
+        ("p1flag", "p1"),
+        ("p2flag", "p2"),
+        ("scxflag", "scx"),
+        ("sheflag", "she"),
+    )
+    for flag_name, param_name in mapping:
+        if getattr(orient_par, flag_name):
+            names.append(param_name)
+    return names
+
+
+def _get_optional_parameter(cal: Calibration, name: str) -> float:
+    """Read one optional calibration parameter from a camera model."""
+    if name == "cc":
+        return float(cal.int_par.cc)
+    if name == "xh":
+        return float(cal.int_par.xh)
+    if name == "yh":
+        return float(cal.int_par.yh)
+
+    added_map = {
+        "k1": 0,
+        "k2": 1,
+        "k3": 2,
+        "p1": 3,
+        "p2": 4,
+        "scx": 5,
+        "she": 6,
+    }
+    if name not in added_map:
+        raise ValueError(f"Unknown optional camera parameter: {name}")
+    return float(cal.added_par[added_map[name]])
+
+
+def _set_optional_parameter(cal: Calibration, name: str, value: float) -> None:
+    """Write one optional calibration parameter into a camera model."""
+    if name == "cc":
+        cal.int_par.cc = value
+        return
+    if name == "xh":
+        cal.int_par.xh = value
+        return
+    if name == "yh":
+        cal.int_par.yh = value
+        return
+
+    added_map = {
+        "k1": 0,
+        "k2": 1,
+        "k3": 2,
+        "p1": 3,
+        "p2": 4,
+        "scx": 5,
+        "she": 6,
+    }
+    if name not in added_map:
+        raise ValueError(f"Unknown optional camera parameter: {name}")
+    cal.added_par[added_map[name]] = value
+
+
+def _glass_basis(glass_vec: np.ndarray) -> Tuple[np.ndarray, np.ndarray, float]:
+    """Build a stable tangent basis around the current glass vector."""
+    norm = float(np.linalg.norm(glass_vec))
+    if norm == 0.0:
+        raise ValueError("Glass vector norm must be non-zero")
+
+    normal = glass_vec / norm
+    helper = (
+        np.array([1.0, 0.0, 0.0]) if abs(normal[0]) < 0.9 else np.array([0.0, 1.0, 0.0])
+    )
+    e1 = np.cross(normal, helper)
+    e1_norm = float(np.linalg.norm(e1))
+    if e1_norm == 0.0:
+        helper = np.array([0.0, 0.0, 1.0])
+        e1 = np.cross(normal, helper)
+        e1_norm = float(np.linalg.norm(e1))
+    e1 /= e1_norm
+    e2 = np.cross(normal, e1)
+    e2 /= float(np.linalg.norm(e2))
+    return e1, e2, norm
+
+
+def _camera_parameter_block(
+    cal: Calibration, optional_names: List[str], include_interface: bool
+) -> np.ndarray:
+    """Pack one camera block for bundle adjustment."""
+    values = [
+        float(cal.ext_par.x0),
+        float(cal.ext_par.y0),
+        float(cal.ext_par.z0),
+        float(cal.ext_par.omega),
+        float(cal.ext_par.phi),
+        float(cal.ext_par.kappa),
+    ]
+    values.extend(_get_optional_parameter(cal, name) for name in optional_names)
+    if include_interface:
+        values.extend([0.0, 0.0])
+    return np.asarray(values, dtype=np.float64)
+
+
+def _apply_camera_parameter_block(
+    cal: Calibration,
+    block: np.ndarray,
+    optional_names: List[str],
+    include_interface: bool,
+    base_glass: Optional[np.ndarray],
+) -> None:
+    """Apply one bundle-adjustment camera block to a calibration object."""
+    cal.set_pos(block[:3])
+    cal.set_angles(block[3:6])
+
+    offset = 6
+    for name in optional_names:
+        _set_optional_parameter(cal, name, float(block[offset]))
+        offset += 1
+
+    if include_interface:
+        if base_glass is None:
+            raise ValueError("Base glass vector is required when interfflag is enabled")
+        e1, e2, scale = _glass_basis(base_glass)
+        cal.glass_par = base_glass + scale * (
+            block[offset] * e1 + block[offset + 1] * e2
+        )
+
+
+def metric_observations_from_pixels(
+    observed_pixels: np.ndarray,
+    cals: List[Calibration],
+    cpar: ControlPar,
+) -> np.ndarray:
+    """Convert pixel observations to flat metric coordinates for ray intersection."""
+    num_points, num_cams, _ = observed_pixels.shape
+    metric_obs = np.full((num_points, num_cams, 2), COORD_UNUSED, dtype=np.float64)
+
+    for pt in range(num_points):
+        for cam in range(num_cams):
+            obs = observed_pixels[pt, cam]
+            if not np.all(np.isfinite(obs)):
+                continue
+            x_metric, y_metric = pixel_to_metric(float(obs[0]), float(obs[1]), cpar)
+            metric_obs[pt, cam] = dist_to_flat(x_metric, y_metric, cals[cam])
+
+    return metric_obs
+
+
+def initialize_bundle_adjustment_points(
+    observed_pixels: np.ndarray,
+    cals: List[Calibration],
+    cpar: ControlPar,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Triangulate 3D starting points from pixel observations and current cameras."""
+    metric_obs = metric_observations_from_pixels(observed_pixels, cals, cpar)
+    num_points, num_cams, _ = metric_obs.shape
+    points = np.empty((num_points, 3), dtype=np.float64)
+    ray_convergence = np.empty(num_points, dtype=np.float64)
+
+    for pt in range(num_points):
+        if np.count_nonzero(metric_obs[pt, :, 0] != COORD_UNUSED) < 2:
+            raise ValueError(
+                "Each point must be observed by at least two cameras for bundle adjustment"
+            )
+        ray_convergence[pt], points[pt] = point_position(
+            metric_obs[pt], num_cams, cpar.mm, cals
+        )
+
+    return points, ray_convergence
+
+
+def reprojection_errors(
+    observed_pixels: np.ndarray,
+    points_3d: np.ndarray,
+    cals: List[Calibration],
+    cpar: ControlPar,
+) -> np.ndarray:
+    """Return per-observation reprojection errors in pixels."""
+    if observed_pixels.ndim != 3 or observed_pixels.shape[2] != 2:
+        raise ValueError("observed_pixels must have shape (num_points, num_cams, 2)")
+    if points_3d.shape != (observed_pixels.shape[0], 3):
+        raise ValueError("points_3d must have shape (num_points, 3)")
+    if observed_pixels.shape[1] != len(cals):
+        raise ValueError(
+            "Number of cameras in observations and calibrations must match"
+        )
+
+    residuals = np.full_like(observed_pixels, np.nan, dtype=np.float64)
+    for pt in range(observed_pixels.shape[0]):
+        for cam in range(observed_pixels.shape[1]):
+            obs = observed_pixels[pt, cam]
+            if not np.all(np.isfinite(obs)):
+                continue
+            x_metric, y_metric = img_coord(points_3d[pt], cals[cam], cpar.mm)
+            proj = metric_to_pixel(x_metric, y_metric, cpar)
+            residuals[pt, cam] = np.asarray(proj, dtype=np.float64) - obs
+
+    return residuals
+
+
+def reprojection_rms(
+    observed_pixels: np.ndarray,
+    points_3d: np.ndarray,
+    cals: List[Calibration],
+    cpar: ControlPar,
+) -> float:
+    """Return the global RMS reprojection error in pixels."""
+    residuals = reprojection_errors(observed_pixels, points_3d, cals, cpar)
+    valid = np.isfinite(residuals)
+    if not np.any(valid):
+        raise ValueError("No valid observations available for reprojection RMS")
+    return float(np.sqrt(np.mean(np.square(residuals[valid]))))
+
+
+def reprojection_rms_per_camera(
+    observed_pixels: np.ndarray,
+    points_3d: np.ndarray,
+    cals: List[Calibration],
+    cpar: ControlPar,
+) -> np.ndarray:
+    """Return per-camera RMS reprojection errors in pixels."""
+    residuals = reprojection_errors(observed_pixels, points_3d, cals, cpar)
+    per_camera = np.full(observed_pixels.shape[1], np.nan, dtype=np.float64)
+    for cam in range(observed_pixels.shape[1]):
+        valid = np.isfinite(residuals[:, cam, :])
+        if np.any(valid):
+            per_camera[cam] = float(
+                np.sqrt(np.mean(np.square(residuals[:, cam, :][valid])))
+            )
+    return per_camera
+
+
+def multi_camera_bundle_adjustment(
+    observed_pixels: np.ndarray,
+    cals: List[Calibration],
+    cpar: ControlPar,
+    orient_par: OrientPar,
+    point_init: Optional[np.ndarray] = None,
+    fix_first_camera: bool = True,
+    fixed_camera_indices: Optional[List[int]] = None,
+    loss: str = "soft_l1",
+    f_scale: float = 1.0,
+    method: str = "trf",
+    prior_sigmas: Optional[Dict[str, float]] = None,
+    max_nfev: Optional[int] = None,
+) -> Tuple[List[Calibration], np.ndarray, scipy.optimize.OptimizeResult]:
+    """Jointly refine multi-camera calibration and 3D points by reprojection error."""
+    observed_pixels = np.asarray(observed_pixels, dtype=np.float64)
+    if observed_pixels.ndim != 3 or observed_pixels.shape[2] != 2:
+        raise ValueError("observed_pixels must have shape (num_points, num_cams, 2)")
+    if len(cals) < 2:
+        raise ValueError("Bundle adjustment requires at least two cameras")
+    if observed_pixels.shape[1] != len(cals):
+        raise ValueError(
+            "Number of cameras in observations and calibrations must match"
+        )
+
+    num_points, num_cams, _ = observed_pixels.shape
+    if num_points == 0:
+        raise ValueError("At least one 3D point is required for bundle adjustment")
+
+    obs_counts = np.count_nonzero(np.all(np.isfinite(observed_pixels), axis=2), axis=1)
+    if np.any(obs_counts < 2):
+        raise ValueError("Each point must be observed by at least two cameras")
+
+    base_cals = [_clone_calibration(cal) for cal in cals]
+    optional_names = _bundle_optional_parameter_names(orient_par)
+    include_interface = bool(orient_par.interfflag)
+    prior_sigmas = {} if prior_sigmas is None else prior_sigmas
+
+    if fixed_camera_indices is None:
+        fixed_camera_indices = [0] if fix_first_camera else []
+
+    fixed_camera_indices = sorted(set(fixed_camera_indices))
+    if any(cam < 0 or cam >= num_cams for cam in fixed_camera_indices):
+        raise ValueError("fixed_camera_indices contains an out-of-range camera index")
+
+    optimized_cam_indices = [
+        cam for cam in range(num_cams) if cam not in fixed_camera_indices
+    ]
+
+    # Refining both 3D points and camera poses from image observations has a similarity
+    # gauge. One fixed camera removes global translation/rotation, but scale still drifts
+    # unless another camera is fixed or translation priors are applied.
+    has_translation_priors = all(
+        prior_sigmas.get(name, 0) > 0 for name in ("x0", "y0", "z0")
+    )
+    if optimized_cam_indices and len(fixed_camera_indices) < 2 and not has_translation_priors:
+        raise ValueError(
+            "Bundle adjustment with free 3D points and only one fixed camera is scale-ambiguous. "
+            "Fix at least two cameras via fixed_camera_indices or provide translation priors "
+            "for x0, y0, and z0."
+        )
+
+    if point_init is None:
+        points0, _ = initialize_bundle_adjustment_points(
+            observed_pixels, base_cals, cpar
+        )
+    else:
+        points0 = np.asarray(point_init, dtype=np.float64)
+        if points0.shape != (num_points, 3):
+            raise ValueError("point_init must have shape (num_points, 3)")
+
+    base_camera_blocks = []
+    base_glass_vectors: Dict[int, np.ndarray] = {}
+    parameter_names = ["x0", "y0", "z0", "omega", "phi", "kappa"] + optional_names
+    if include_interface:
+        parameter_names.extend(["glass_e1", "glass_e2"])
+
+    initial_blocks = []
+    for cam in optimized_cam_indices:
+        initial_block = _camera_parameter_block(
+            base_cals[cam], optional_names, include_interface
+        )
+        initial_blocks.append(initial_block)
+        base_camera_blocks.append(initial_block.copy())
+        if include_interface:
+            base_glass_vectors[cam] = base_cals[cam].glass_par.copy()
+
+    if initial_blocks:
+        x0 = np.concatenate(initial_blocks + [points0.ravel()])
+    else:
+        x0 = points0.ravel().copy()
+
+    camera_block_size = 6 + len(optional_names) + (2 if include_interface else 0)
+    point_offset = camera_block_size * len(optimized_cam_indices)
+
+    def unpack_parameters(params: np.ndarray) -> Tuple[List[Calibration], np.ndarray]:
+        trial_cals = [_clone_calibration(cal) for cal in base_cals]
+        offset = 0
+        for cam in optimized_cam_indices:
+            block = params[offset : offset + camera_block_size]
+            _apply_camera_parameter_block(
+                trial_cals[cam],
+                block,
+                optional_names,
+                include_interface,
+                base_glass_vectors.get(cam),
+            )
+            offset += camera_block_size
+
+        points = params[point_offset:].reshape(num_points, 3)
+        return trial_cals, points
+
+    def residual_vector(params: np.ndarray) -> np.ndarray:
+        trial_cals, points = unpack_parameters(params)
+        residuals = []
+        for pt in range(num_points):
+            for cam in range(num_cams):
+                obs = observed_pixels[pt, cam]
+                if not np.all(np.isfinite(obs)):
+                    continue
+                x_metric, y_metric = img_coord(points[pt], trial_cals[cam], cpar.mm)
+                proj_x, proj_y = metric_to_pixel(x_metric, y_metric, cpar)
+                residuals.append((proj_x - obs[0]) / cpar.pix_x)
+                residuals.append((proj_y - obs[1]) / cpar.pix_y)
+
+        if prior_sigmas:
+            offset = 0
+            for cam_index, cam in enumerate(optimized_cam_indices):
+                block = params[offset : offset + camera_block_size]
+                base_block = base_camera_blocks[cam_index]
+                for name, value, base_value in zip(parameter_names, block, base_block):
+                    sigma = prior_sigmas.get(name)
+                    if sigma is None or sigma <= 0:
+                        continue
+                    residuals.append((value - base_value) / sigma)
+                offset += camera_block_size
+
+        return np.asarray(residuals, dtype=np.float64)
+
+    initial_cals, initial_points = unpack_parameters(x0)
+    initial_rms = reprojection_rms(observed_pixels, initial_points, initial_cals, cpar)
+    initial_per_camera = reprojection_rms_per_camera(
+        observed_pixels, initial_points, initial_cals, cpar
+    )
+
+    result = scipy.optimize.least_squares(
+        residual_vector,
+        x0,
+        method=method,
+        loss=loss,
+        f_scale=f_scale,
+        max_nfev=max_nfev,
+    )
+
+    refined_cals, refined_points = unpack_parameters(result.x)
+    result["initial_reprojection_rms"] = initial_rms
+    result["final_reprojection_rms"] = reprojection_rms(
+        observed_pixels, refined_points, refined_cals, cpar
+    )
+    result["initial_reprojection_rms_per_camera"] = initial_per_camera
+    result["final_reprojection_rms_per_camera"] = reprojection_rms_per_camera(
+        observed_pixels, refined_points, refined_cals, cpar
+    )
+    result["optimized_camera_indices"] = optimized_cam_indices
+
+    return refined_cals, refined_points, result
diff --git a/pyproject.toml b/pyproject.toml
index f5946bd..7ddceb8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,7 +27,7 @@ dependencies = [
   "scipy>=1.17.1",
   "sphinx>=9.1.0",
   "sphinx-autoapi>=3.7.0",
-  "types-pyyaml>=6.0.12.20250915",
+  "types-pyyaml>=6.0.12.20250915"
 ]
 description = "Python version of the OpenPTV library"
 dynamic = ["version"]
diff --git a/tests/test_bundle_adjustment.py b/tests/test_bundle_adjustment.py
new file mode 100644
index 0000000..2215192
--- /dev/null
+++ b/tests/test_bundle_adjustment.py
@@ -0,0 +1,204 @@
+import unittest
+from pathlib import Path
+
+import numpy as np
+
+from openptv_python.calibration import Calibration, read_calibration
+from openptv_python.imgcoord import image_coordinates
+from openptv_python.orientation import (
+    multi_camera_bundle_adjustment,
+    reprojection_rms,
+)
+from openptv_python.parameters import ControlPar, OrientPar
+from openptv_python.tracking_frame_buf import read_path_frame, read_targets
+from openptv_python.trafo import arr_metric_to_pixel
+
+
+class TestBundleAdjustment(unittest.TestCase):
+    def setUp(self):
+        self.control = ControlPar(4).from_file(
+            Path("tests/testing_folder/control_parameters/control.par")
+        )
+        self.add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
+        self.true_cals = [
+            read_calibration(
+                Path(
+                    f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"
+                ),
+                self.add_file,
+            )
+            for cam_num in range(1, 5)
+        ]
+
+    @staticmethod
+    def clone_calibration(cal: Calibration) -> Calibration:
+        return Calibration(
+            ext_par=cal.ext_par.copy(),
+            int_par=cal.int_par.copy(),
+            glass_par=cal.glass_par.copy(),
+            added_par=cal.added_par.copy(),
+            mmlut=cal.mmlut,
+            mmlut_data=cal.mmlut_data,
+        )
+
+    def perturb_calibrations(self, true_cals: list[Calibration]) -> list[Calibration]:
+        perturbed = [self.clone_calibration(cal) for cal in true_cals]
+        deltas = [
+            (np.array([0.0, 0.0, 0.0]), np.array([0.0, 0.0, 0.0])),
+            (np.array([1.2, -0.8, 0.7]), np.array([0.012, -0.010, 0.006])),
+            (np.array([-0.9, 0.7, -0.6]), np.array([-0.009, 0.008, -0.005])),
+            (np.array([0.8, 1.0, -0.5]), np.array([0.011, 0.007, -0.004])),
+        ]
+        for cal, (pos_delta, angle_delta) in zip(perturbed, deltas):
+            cal.set_pos(cal.get_pos() + pos_delta)
+            cal.set_angles(cal.get_angles() + angle_delta)
+        return perturbed
+
+    def lightly_perturb_calibrations(self, true_cals: list[Calibration]) -> list[Calibration]:
+        perturbed = [self.clone_calibration(cal) for cal in true_cals]
+        deltas = [
+            (np.array([0.0, 0.0, 0.0]), np.array([0.0, 0.0, 0.0])),
+            (np.array([0.5, -0.3, 0.2]), np.array([0.004, -0.003, 0.002])),
+            (np.array([-0.4, 0.3, -0.2]), np.array([-0.003, 0.003, -0.002])),
+            (np.array([0.3, 0.4, -0.2]), np.array([0.003, 0.002, -0.002])),
+        ]
+        for cal, (pos_delta, angle_delta) in zip(perturbed, deltas):
+            cal.set_pos(cal.get_pos() + pos_delta)
+            cal.set_angles(cal.get_angles() + angle_delta)
+        return perturbed
+
+    def test_multi_camera_bundle_adjustment_improves_synthetic_reprojection(self):
+        xs = np.linspace(-20.0, 20.0, 3)
+        ys = np.linspace(-15.0, 15.0, 2)
+        zs = np.array([-4.0, 5.0])
+        points = np.array([[x, y, z] for x in xs for y in ys for z in zs], dtype=float)
+
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        start_cals = self.perturb_calibrations(self.true_cals)
+        refined_cals, refined_points, result = multi_camera_bundle_adjustment(
+            observed_pixels,
+            start_cals,
+            self.control,
+            OrientPar(),
+            point_init=None,
+            fixed_camera_indices=[0, 1],
+            loss="linear",
+            method="lm",
+            prior_sigmas={
+                "x0": 1.0,
+                "y0": 1.0,
+                "z0": 1.0,
+                "omega": 0.01,
+                "phi": 0.01,
+                "kappa": 0.01,
+            },
+            max_nfev=50,
+        )
+
+        self.assertTrue(result.success, msg=result.message)
+        before_rms = result["initial_reprojection_rms"]
+        after_rms = result["final_reprojection_rms"]
+        self.assertLess(after_rms, before_rms * 0.4)
+        self.assertLess(after_rms, 3e-2)
+
+    def test_cavity_reprojection_improves(self):
+        cavity_dir = Path("tests/testing_fodder/test_cavity")
+        control = ControlPar(4).from_file(cavity_dir / "parameters/ptv.par")
+        true_cals = [
+            read_calibration(
+                cavity_dir / f"cal/cam{cam_num}.tif.ori",
+                cavity_dir / f"cal/cam{cam_num}.tif.addpar",
+            )
+            for cam_num in range(1, 5)
+        ]
+        start_cals = self.lightly_perturb_calibrations(true_cals)
+
+        cor_buf, path_buf = read_path_frame(
+            str(cavity_dir / "res_orig/rt_is"),
+            "",
+            "",
+            10001,
+        )
+        targets = [
+            read_targets(str(cavity_dir / f"img_orig/cam{cam_num}.%05d"), 10001)
+            for cam_num in range(1, 5)
+        ]
+
+        subset = [
+            pt_num
+            for pt_num, corres in enumerate(cor_buf)
+            if np.all(corres.p >= 0)
+        ][:12]
+        observed_pixels = np.full((len(subset), 4, 2), np.nan, dtype=float)
+        point_init = np.empty((len(subset), 3), dtype=float)
+        for out_num, pt_num in enumerate(subset):
+            path_info = path_buf[pt_num]
+            point_init[out_num] = path_info.x
+            for cam in range(4):
+                target_index = cor_buf[pt_num].p[cam]
+                if target_index < 0:
+                    continue
+                observed_pixels[out_num, cam, 0] = targets[cam][target_index].x
+                observed_pixels[out_num, cam, 1] = targets[cam][target_index].y
+
+        before_rms = reprojection_rms(observed_pixels, point_init, start_cals, control)
+        refined_cals, refined_points, result = multi_camera_bundle_adjustment(
+            observed_pixels,
+            start_cals,
+            control,
+            OrientPar(),
+            point_init=point_init,
+            fixed_camera_indices=[0, 1],
+            loss="linear",
+            method="lm",
+            max_nfev=80,
+        )
+        after_rms = reprojection_rms(observed_pixels, refined_points, refined_cals, control)
+
+        print(
+            f"test_cavity reprojection RMS improved from {before_rms:.6f} px to {after_rms:.6f} px"
+        )
+
+        self.assertTrue(result.success, msg=result.message)
+        self.assertLess(after_rms, before_rms * 0.6)
+        self.assertLess(after_rms, before_rms - 0.2)
+        np.testing.assert_allclose(after_rms, result["final_reprojection_rms"])
+
+    def test_bundle_adjustment_rejects_scale_ambiguous_configuration(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        with self.assertRaises(ValueError):
+            multi_camera_bundle_adjustment(
+                observed_pixels,
+                self.perturb_calibrations(self.true_cals),
+                self.control,
+                OrientPar(),
+                point_init=None,
+                fix_first_camera=True,
+                loss="linear",
+                method="lm",
+                max_nfev=10,
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_calibration_compare.py b/tests/test_calibration_compare.py
new file mode 100644
index 0000000..ff5dfc9
--- /dev/null
+++ b/tests/test_calibration_compare.py
@@ -0,0 +1,64 @@
+import tempfile
+import unittest
+from pathlib import Path
+
+import numpy as np
+
+from openptv_python.calibration import read_calibration, write_calibration
+from openptv_python.calibration_compare import (
+    compare_calibration_folders,
+    format_calibration_comparison,
+)
+
+
+class TestCalibrationCompare(unittest.TestCase):
+    def test_compare_same_folder_is_zero(self):
+        cavity_cal_dir = Path("tests/testing_fodder/test_cavity/cal")
+        deltas = compare_calibration_folders(cavity_cal_dir, cavity_cal_dir)
+
+        self.assertEqual(sorted(deltas.keys()), [f"cam{i}.tif" for i in range(1, 5)])
+        for delta in deltas.values():
+            np.testing.assert_allclose(delta.position_delta, 0.0)
+            np.testing.assert_allclose(delta.angle_delta, 0.0)
+            np.testing.assert_allclose(delta.primary_point_delta, 0.0)
+            np.testing.assert_allclose(delta.glass_delta, 0.0)
+            np.testing.assert_allclose(delta.added_par_delta, 0.0)
+
+    def test_compare_modified_folder_reports_numeric_deltas(self):
+        cavity_cal_dir = Path("tests/testing_fodder/test_cavity/cal")
+
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            tmp_dir = Path(tmp_dir_name)
+            for cam_num in range(1, 5):
+                cal = read_calibration(
+                    cavity_cal_dir / f"cam{cam_num}.tif.ori",
+                    cavity_cal_dir / f"cam{cam_num}.tif.addpar",
+                )
+                if cam_num == 2:
+                    cal.set_pos(cal.get_pos() + np.array([1.0, -2.0, 3.0]))
+                    cal.set_angles(cal.get_angles() + np.array([0.01, -0.02, 0.03]))
+                    cal.added_par[0] += 1.5e-4
+                    cal.added_par[3] -= 2.5e-4
+                write_calibration(
+                    cal,
+                    tmp_dir / f"cam{cam_num}.tif.ori",
+                    tmp_dir / f"cam{cam_num}.tif.addpar",
+                )
+
+            deltas = compare_calibration_folders(cavity_cal_dir, tmp_dir)
+            cam2 = deltas["cam2.tif"]
+            np.testing.assert_allclose(cam2.position_delta, [1.0, -2.0, 3.0])
+            np.testing.assert_allclose(cam2.angle_delta, [0.01, -0.02, 0.03])
+            np.testing.assert_allclose(
+                cam2.added_par_delta,
+                [1.5e-4, 0.0, 0.0, -2.5e-4, 0.0, 0.0, 0.0],
+            )
+
+            rendered = format_calibration_comparison(deltas)
+            self.assertIn("cam2.tif:", rendered)
+            self.assertIn("+1.000000000 -2.000000000 +3.000000000", rendered)
+            self.assertIn("+0.000150000", rendered)
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/tests/testing_fodder/test_cavity/parameters_Run1.yaml b/tests/testing_fodder/test_cavity/parameters_Run1.yaml
new file mode 100644
index 0000000..9c58258
--- /dev/null
+++ b/tests/testing_fodder/test_cavity/parameters_Run1.yaml
@@ -0,0 +1,174 @@
+num_cams: 4
+plugins:
+  available_tracking:
+  - default
+  available_sequence:
+  - default
+  selected_tracking: default
+  selected_sequence: default
+cal_ori:
+  chfield: 0
+  fixp_name: cal/target_on_a_side.txt
+  img_cal_name:
+  - cal/cam1.tif
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index a4c40c8..6cb65f9 100644
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From 55a837f252db22214b188cb18afe248ce64f9457 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Fri, 6 Mar 2026 20:54:25 +0200
Subject: [PATCH 06/27] added bundle adjustment

---
 openptv_python/calibration_compare.py |    8 +-
 openptv_python/orientation.py         |  268 ++++-
 tests/test_bundle_adjustment.py       |  219 ++++-
 tests/test_calibration_compare.py     |    2 +-
 uv.lock                               | 1294 ++++++++++++-------------
 5 files changed, 1118 insertions(+), 673 deletions(-)

diff --git a/openptv_python/calibration_compare.py b/openptv_python/calibration_compare.py
index 28e79b0..2490c9d 100644
--- a/openptv_python/calibration_compare.py
+++ b/openptv_python/calibration_compare.py
@@ -107,7 +107,8 @@ def format_calibration_comparison(
         delta = deltas[camera_key]
         lines.append(f"{camera_key}:")
         lines.append(
-            "  position_delta: " + " ".join(f"{value:+.9f}" for value in delta.position_delta)
+            "  position_delta: "
+            + " ".join(f"{value:+.9f}" for value in delta.position_delta)
         )
         lines.append(
             "  angle_delta: " + " ".join(f"{value:+.9f}" for value in delta.angle_delta)
@@ -120,7 +121,8 @@ def format_calibration_comparison(
             "  glass_delta: " + " ".join(f"{value:+.9f}" for value in delta.glass_delta)
         )
         lines.append(
-            "  addpar_delta: " + " ".join(f"{value:+.9f}" for value in delta.added_par_delta)
+            "  addpar_delta: "
+            + " ".join(f"{value:+.9f}" for value in delta.added_par_delta)
         )
 
     return "\n".join(lines)
@@ -147,4 +149,4 @@ def main(argv: Iterable[str] | None = None) -> int:
 
 
 if __name__ == "__main__":
-    raise SystemExit(main())
\ No newline at end of file
+    raise SystemExit(main())
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index d92cb9b..1f914df 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -1316,6 +1316,45 @@ def reprojection_rms_per_camera(
     return per_camera
 
 
+def mean_ray_convergence(
+    observed_pixels: np.ndarray,
+    cals: List[Calibration],
+    cpar: ControlPar,
+) -> float:
+    """Return the mean ray convergence from triangulating observed pixels."""
+    _, ray_convergence = initialize_bundle_adjustment_points(
+        observed_pixels, cals, cpar
+    )
+    return float(np.mean(ray_convergence))
+
+
+def _expand_parameter_limits(
+    parameter_names: List[str],
+    limits: Optional[Dict[str, Tuple[float, float]]],
+    base_blocks: List[np.ndarray],
+    repeat: int,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Expand per-parameter bounds for each optimized camera block."""
+    lower = []
+    upper = []
+
+    if repeat <= 0:
+        return np.empty(0, dtype=np.float64), np.empty(0, dtype=np.float64)
+
+    for block_index in range(repeat):
+        for param_index, name in enumerate(parameter_names):
+            if limits is None or name not in limits:
+                lower.append(-np.inf)
+                upper.append(np.inf)
+                continue
+            low_delta, high_delta = limits[name]
+            base_value = base_blocks[block_index][param_index]
+            lower.append(base_value + low_delta)
+            upper.append(base_value + high_delta)
+
+    return np.asarray(lower, dtype=np.float64), np.asarray(upper, dtype=np.float64)
+
+
 def multi_camera_bundle_adjustment(
     observed_pixels: np.ndarray,
     cals: List[Calibration],
@@ -1328,7 +1367,13 @@ def multi_camera_bundle_adjustment(
     f_scale: float = 1.0,
     method: str = "trf",
     prior_sigmas: Optional[Dict[str, float]] = None,
+    parameter_bounds: Optional[Dict[str, Tuple[float, float]]] = None,
     max_nfev: Optional[int] = None,
+    optimize_extrinsics: bool = True,
+    optimize_points: bool = True,
+    ftol: Optional[float] = None,
+    xtol: Optional[float] = None,
+    gtol: Optional[float] = None,
 ) -> Tuple[List[Calibration], np.ndarray, scipy.optimize.OptimizeResult]:
     """Jointly refine multi-camera calibration and 3D points by reprojection error."""
     observed_pixels = np.asarray(observed_pixels, dtype=np.float64)
@@ -1361,9 +1406,20 @@ def multi_camera_bundle_adjustment(
     if any(cam < 0 or cam >= num_cams for cam in fixed_camera_indices):
         raise ValueError("fixed_camera_indices contains an out-of-range camera index")
 
-    optimized_cam_indices = [
-        cam for cam in range(num_cams) if cam not in fixed_camera_indices
-    ]
+    if optimize_extrinsics:
+        optimized_cam_indices = [
+            cam for cam in range(num_cams) if cam not in fixed_camera_indices
+        ]
+    else:
+        optimized_cam_indices = list(range(num_cams))
+
+    if not optimize_extrinsics and not optional_names and not include_interface:
+        raise ValueError("No camera parameters are enabled for optimization")
+
+    if not optimize_points and not optimized_cam_indices:
+        raise ValueError(
+            "Bundle adjustment must optimize points or at least one camera"
+        )
 
     # Refining both 3D points and camera poses from image observations has a similarity
     # gauge. One fixed camera removes global translation/rotation, but scale still drifts
@@ -1371,7 +1427,13 @@ def multi_camera_bundle_adjustment(
     has_translation_priors = all(
         prior_sigmas.get(name, 0) > 0 for name in ("x0", "y0", "z0")
     )
-    if optimized_cam_indices and len(fixed_camera_indices) < 2 and not has_translation_priors:
+    if (
+        optimize_points
+        and optimized_cam_indices
+        and optimize_extrinsics
+        and len(fixed_camera_indices) < 2
+        and not has_translation_priors
+    ):
         raise ValueError(
             "Bundle adjustment with free 3D points and only one fixed camera is scale-ambiguous. "
             "Fix at least two cameras via fixed_camera_indices or provide translation priors "
@@ -1389,7 +1451,10 @@ def multi_camera_bundle_adjustment(
 
     base_camera_blocks = []
     base_glass_vectors: Dict[int, np.ndarray] = {}
-    parameter_names = ["x0", "y0", "z0", "omega", "phi", "kappa"] + optional_names
+    parameter_names = []
+    if optimize_extrinsics:
+        parameter_names.extend(["x0", "y0", "z0", "omega", "phi", "kappa"])
+    parameter_names.extend(optional_names)
     if include_interface:
         parameter_names.extend(["glass_e1", "glass_e2"])
 
@@ -1398,24 +1463,50 @@ def multi_camera_bundle_adjustment(
         initial_block = _camera_parameter_block(
             base_cals[cam], optional_names, include_interface
         )
+        if not optimize_extrinsics:
+            initial_block = initial_block[6:]
         initial_blocks.append(initial_block)
         base_camera_blocks.append(initial_block.copy())
         if include_interface:
             base_glass_vectors[cam] = base_cals[cam].glass_par.copy()
 
+    camera_block_size = len(parameter_names)
+    payloads = []
     if initial_blocks:
-        x0 = np.concatenate(initial_blocks + [points0.ravel()])
+        payloads.extend(initial_blocks)
+    if optimize_points:
+        payloads.append(points0.ravel())
+    if payloads:
+        x0 = np.concatenate(payloads)
     else:
-        x0 = points0.ravel().copy()
+        x0 = np.empty(0, dtype=np.float64)
 
-    camera_block_size = 6 + len(optional_names) + (2 if include_interface else 0)
     point_offset = camera_block_size * len(optimized_cam_indices)
 
+    camera_lower, camera_upper = _expand_parameter_limits(
+        parameter_names,
+        parameter_bounds,
+        base_camera_blocks,
+        len(optimized_cam_indices),
+    )
+    if optimize_points:
+        point_lower = np.full(points0.size, -np.inf, dtype=np.float64)
+        point_upper = np.full(points0.size, np.inf, dtype=np.float64)
+        bounds = (
+            np.concatenate([camera_lower, point_lower]),
+            np.concatenate([camera_upper, point_upper]),
+        )
+    else:
+        bounds = (camera_lower, camera_upper)
+
     def unpack_parameters(params: np.ndarray) -> Tuple[List[Calibration], np.ndarray]:
         trial_cals = [_clone_calibration(cal) for cal in base_cals]
         offset = 0
         for cam in optimized_cam_indices:
             block = params[offset : offset + camera_block_size]
+            if not optimize_extrinsics:
+                base_pose = _camera_parameter_block(base_cals[cam], [], False)[:6]
+                block = np.concatenate([base_pose, block])
             _apply_camera_parameter_block(
                 trial_cals[cam],
                 block,
@@ -1425,7 +1516,10 @@ def unpack_parameters(params: np.ndarray) -> Tuple[List[Calibration], np.ndarray
             )
             offset += camera_block_size
 
-        points = params[point_offset:].reshape(num_points, 3)
+        if optimize_points:
+            points = params[point_offset:].reshape(num_points, 3)
+        else:
+            points = points0.copy()
         return trial_cals, points
 
     def residual_vector(params: np.ndarray) -> np.ndarray:
@@ -1461,13 +1555,24 @@ def residual_vector(params: np.ndarray) -> np.ndarray:
         observed_pixels, initial_points, initial_cals, cpar
     )
 
+    least_squares_kwargs = {
+        "method": method,
+        "loss": loss,
+        "f_scale": f_scale,
+        "max_nfev": max_nfev,
+        "bounds": bounds,
+    }
+    if ftol is not None:
+        least_squares_kwargs["ftol"] = ftol
+    if xtol is not None:
+        least_squares_kwargs["xtol"] = xtol
+    if gtol is not None:
+        least_squares_kwargs["gtol"] = gtol
+
     result = scipy.optimize.least_squares(
         residual_vector,
         x0,
-        method=method,
-        loss=loss,
-        f_scale=f_scale,
-        max_nfev=max_nfev,
+        **least_squares_kwargs,
     )
 
     refined_cals, refined_points = unpack_parameters(result.x)
@@ -1482,3 +1587,140 @@ def residual_vector(params: np.ndarray) -> np.ndarray:
     result["optimized_camera_indices"] = optimized_cam_indices
 
     return refined_cals, refined_points, result
+
+
+def guarded_two_step_bundle_adjustment(
+    observed_pixels: np.ndarray,
+    cals: List[Calibration],
+    cpar: ControlPar,
+    pose_orient_par: OrientPar,
+    intrinsic_orient_par: OrientPar,
+    *,
+    point_init: Optional[np.ndarray] = None,
+    fixed_camera_indices: Optional[List[int]] = None,
+    pose_prior_sigmas: Optional[Dict[str, float]] = None,
+    pose_parameter_bounds: Optional[Dict[str, Tuple[float, float]]] = None,
+    pose_loss: str = "linear",
+    pose_method: str = "trf",
+    pose_max_nfev: Optional[int] = None,
+    intrinsic_prior_sigmas: Optional[Dict[str, float]] = None,
+    intrinsic_parameter_bounds: Optional[Dict[str, Tuple[float, float]]] = None,
+    intrinsic_loss: str = "linear",
+    intrinsic_method: str = "trf",
+    intrinsic_max_nfev: Optional[int] = None,
+    intrinsic_ftol: Optional[float] = 1e-12,
+    intrinsic_xtol: Optional[float] = 1e-12,
+    intrinsic_gtol: Optional[float] = 1e-12,
+    pose_optimize_points: bool = True,
+    intrinsic_optimize_points: bool = True,
+    reject_worse_solution: bool = True,
+    reject_on_ray_convergence: bool = True,
+) -> Tuple[List[Calibration], np.ndarray, Dict[str, object]]:
+    """Run pose-only BA then tightly constrained intrinsics BA with acceptance checks."""
+    base_cals = [_clone_calibration(cal) for cal in cals]
+    if point_init is None:
+        base_points, _ = initialize_bundle_adjustment_points(
+            observed_pixels, base_cals, cpar
+        )
+    else:
+        base_points = np.asarray(point_init, dtype=np.float64)
+        if base_points.shape != (observed_pixels.shape[0], 3):
+            raise ValueError("point_init must have shape (num_points, 3)")
+
+    baseline_rms = reprojection_rms(observed_pixels, base_points, base_cals, cpar)
+    baseline_ray_convergence = mean_ray_convergence(observed_pixels, base_cals, cpar)
+
+    pose_cals, pose_points, pose_result = multi_camera_bundle_adjustment(
+        observed_pixels,
+        base_cals,
+        cpar,
+        pose_orient_par,
+        point_init=base_points,
+        fixed_camera_indices=fixed_camera_indices,
+        loss=pose_loss,
+        method=pose_method,
+        prior_sigmas=pose_prior_sigmas,
+        parameter_bounds=pose_parameter_bounds,
+        max_nfev=pose_max_nfev,
+        optimize_extrinsics=True,
+        optimize_points=pose_optimize_points,
+    )
+    pose_rms = reprojection_rms(observed_pixels, pose_points, pose_cals, cpar)
+    pose_ray_convergence = mean_ray_convergence(observed_pixels, pose_cals, cpar)
+
+    intrinsic_fixed = list(range(len(cals)))
+    intrinsic_cals, intrinsic_points, intrinsic_result = multi_camera_bundle_adjustment(
+        observed_pixels,
+        pose_cals,
+        cpar,
+        intrinsic_orient_par,
+        point_init=pose_points,
+        fixed_camera_indices=intrinsic_fixed,
+        loss=intrinsic_loss,
+        method=intrinsic_method,
+        prior_sigmas=intrinsic_prior_sigmas,
+        parameter_bounds=intrinsic_parameter_bounds,
+        max_nfev=intrinsic_max_nfev,
+        optimize_extrinsics=False,
+        optimize_points=intrinsic_optimize_points,
+        ftol=intrinsic_ftol,
+        xtol=intrinsic_xtol,
+        gtol=intrinsic_gtol,
+    )
+    intrinsic_rms = reprojection_rms(
+        observed_pixels, intrinsic_points, intrinsic_cals, cpar
+    )
+    intrinsic_ray_convergence = mean_ray_convergence(
+        observed_pixels, intrinsic_cals, cpar
+    )
+
+    accepted_stage = "intrinsics"
+    final_cals = intrinsic_cals
+    final_points = intrinsic_points
+    final_rms = intrinsic_rms
+    final_ray_convergence = intrinsic_ray_convergence
+
+    pose_ok = pose_rms <= baseline_rms and (
+        not reject_on_ray_convergence
+        or pose_ray_convergence <= baseline_ray_convergence
+    )
+    intrinsic_ok = intrinsic_rms <= pose_rms and (
+        not reject_on_ray_convergence
+        or intrinsic_ray_convergence <= pose_ray_convergence
+    )
+
+    if reject_worse_solution:
+        if not pose_ok:
+            accepted_stage = "baseline"
+            final_cals = base_cals
+            final_points = base_points
+            final_rms = baseline_rms
+            final_ray_convergence = baseline_ray_convergence
+        elif not intrinsic_ok:
+            accepted_stage = "pose"
+            final_cals = pose_cals
+            final_points = pose_points
+            final_rms = pose_rms
+            final_ray_convergence = pose_ray_convergence
+
+    summary = {
+        "baseline_reprojection_rms": baseline_rms,
+        "baseline_mean_ray_convergence": baseline_ray_convergence,
+        "baseline_cals": base_cals,
+        "baseline_points": base_points,
+        "pose_reprojection_rms": pose_rms,
+        "pose_mean_ray_convergence": pose_ray_convergence,
+        "pose_cals": pose_cals,
+        "pose_points": pose_points,
+        "intrinsic_reprojection_rms": intrinsic_rms,
+        "intrinsic_mean_ray_convergence": intrinsic_ray_convergence,
+        "intrinsic_cals": intrinsic_cals,
+        "intrinsic_points": intrinsic_points,
+        "accepted_stage": accepted_stage,
+        "final_reprojection_rms": final_rms,
+        "final_mean_ray_convergence": final_ray_convergence,
+        "pose_result": pose_result,
+        "intrinsic_result": intrinsic_result,
+    }
+
+    return final_cals, final_points, summary
diff --git a/tests/test_bundle_adjustment.py b/tests/test_bundle_adjustment.py
index 2215192..cb84caf 100644
--- a/tests/test_bundle_adjustment.py
+++ b/tests/test_bundle_adjustment.py
@@ -6,10 +6,12 @@
 from openptv_python.calibration import Calibration, read_calibration
 from openptv_python.imgcoord import image_coordinates
 from openptv_python.orientation import (
+    guarded_two_step_bundle_adjustment,
+    mean_ray_convergence,
     multi_camera_bundle_adjustment,
     reprojection_rms,
 )
-from openptv_python.parameters import ControlPar, OrientPar
+from openptv_python.parameters import ControlPar, OrientPar, SequencePar
 from openptv_python.tracking_frame_buf import read_path_frame, read_targets
 from openptv_python.trafo import arr_metric_to_pixel
 
@@ -22,9 +24,7 @@ def setUp(self):
         self.add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
         self.true_cals = [
             read_calibration(
-                Path(
-                    f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"
-                ),
+                Path(f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"),
                 self.add_file,
             )
             for cam_num in range(1, 5)
@@ -54,7 +54,9 @@ def perturb_calibrations(self, true_cals: list[Calibration]) -> list[Calibration
             cal.set_angles(cal.get_angles() + angle_delta)
         return perturbed
 
-    def lightly_perturb_calibrations(self, true_cals: list[Calibration]) -> list[Calibration]:
+    def lightly_perturb_calibrations(
+        self, true_cals: list[Calibration]
+    ) -> list[Calibration]:
         perturbed = [self.clone_calibration(cal) for cal in true_cals]
         deltas = [
             (np.array([0.0, 0.0, 0.0]), np.array([0.0, 0.0, 0.0])),
@@ -67,6 +69,41 @@ def lightly_perturb_calibrations(self, true_cals: list[Calibration]) -> list[Cal
             cal.set_angles(cal.get_angles() + angle_delta)
         return perturbed
 
+    @staticmethod
+    def cavity_quadruplet_observations(cavity_dir: Path, control: ControlPar):
+        seq = SequencePar.from_file(cavity_dir / "parameters/sequence.par", 4)
+        observed_batches = []
+        point_batches = []
+
+        for frame in range(seq.first, seq.last + 1):
+            cor_buf, path_buf = read_path_frame(
+                str(cavity_dir / "res_orig/rt_is"),
+                "",
+                "",
+                frame,
+            )
+            targets = [
+                read_targets(str(cavity_dir / f"img_orig/cam{cam_num}.%05d"), frame)
+                for cam_num in range(1, 5)
+            ]
+            subset = [
+                pt_num for pt_num, corres in enumerate(cor_buf) if np.all(corres.p >= 0)
+            ]
+            observed_pixels = np.full((len(subset), 4, 2), np.nan, dtype=float)
+            point_init = np.empty((len(subset), 3), dtype=float)
+            for out_num, pt_num in enumerate(subset):
+                point_init[out_num] = path_buf[pt_num].x
+                for cam in range(4):
+                    target_index = cor_buf[pt_num].p[cam]
+                    observed_pixels[out_num, cam, 0] = targets[cam][target_index].x
+                    observed_pixels[out_num, cam, 1] = targets[cam][target_index].y
+            observed_batches.append(observed_pixels)
+            point_batches.append(point_init)
+
+        return np.concatenate(observed_batches, axis=0), np.concatenate(
+            point_batches, axis=0
+        )
+
     def test_multi_camera_bundle_adjustment_improves_synthetic_reprojection(self):
         xs = np.linspace(-20.0, 20.0, 3)
         ys = np.linspace(-15.0, 15.0, 2)
@@ -130,9 +167,7 @@ def test_cavity_reprojection_improves(self):
         ]
 
         subset = [
-            pt_num
-            for pt_num, corres in enumerate(cor_buf)
-            if np.all(corres.p >= 0)
+            pt_num for pt_num, corres in enumerate(cor_buf) if np.all(corres.p >= 0)
         ][:12]
         observed_pixels = np.full((len(subset), 4, 2), np.nan, dtype=float)
         point_init = np.empty((len(subset), 3), dtype=float)
@@ -158,7 +193,9 @@ def test_cavity_reprojection_improves(self):
             method="lm",
             max_nfev=80,
         )
-        after_rms = reprojection_rms(observed_pixels, refined_points, refined_cals, control)
+        after_rms = reprojection_rms(
+            observed_pixels, refined_points, refined_cals, control
+        )
 
         print(
             f"test_cavity reprojection RMS improved from {before_rms:.6f} px to {after_rms:.6f} px"
@@ -199,6 +236,170 @@ def test_bundle_adjustment_rejects_scale_ambiguous_configuration(self):
                 max_nfev=10,
             )
 
+    def test_guarded_two_step_bundle_adjustment_rejects_bad_intrinsics_stage(self):
+        cavity_dir = Path("tests/testing_fodder/test_cavity")
+        control = ControlPar(4).from_file(cavity_dir / "parameters/ptv.par")
+        true_cals = [
+            read_calibration(
+                cavity_dir / f"cal/cam{cam_num}.tif.ori",
+                cavity_dir / f"cal/cam{cam_num}.tif.addpar",
+            )
+            for cam_num in range(1, 5)
+        ]
+        observed_pixels, point_init = self.cavity_quadruplet_observations(
+            cavity_dir, control
+        )
+        start_cals = self.lightly_perturb_calibrations(true_cals)
+
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+
+        final_cals, final_points, summary = guarded_two_step_bundle_adjustment(
+            observed_pixels,
+            start_cals,
+            control,
+            OrientPar(),
+            intrinsics,
+            point_init=point_init,
+            fixed_camera_indices=[0, 1],
+            pose_prior_sigmas={
+                "x0": 0.5,
+                "y0": 0.5,
+                "z0": 0.5,
+                "omega": 0.005,
+                "phi": 0.005,
+                "kappa": 0.005,
+            },
+            pose_parameter_bounds={
+                "x0": (-2.0, 2.0),
+                "y0": (-2.0, 2.0),
+                "z0": (-2.0, 2.0),
+                "omega": (-0.02, 0.02),
+                "phi": (-0.02, 0.02),
+                "kappa": (-0.02, 0.02),
+            },
+            pose_max_nfev=60,
+            intrinsic_prior_sigmas={
+                "k1": 1e-12,
+                "k2": 1e-12,
+                "k3": 1e-12,
+                "p1": 1e-12,
+                "p2": 1e-12,
+                "scx": 1e-12,
+                "she": 1e-12,
+                "cc": 1e-12,
+                "xh": 1e-12,
+                "yh": 1e-12,
+            },
+            intrinsic_parameter_bounds={
+                "k1": (-1e-10, 1e-10),
+                "k2": (-1e-10, 1e-10),
+                "k3": (-1e-10, 1e-10),
+                "p1": (-1e-10, 1e-10),
+                "p2": (-1e-10, 1e-10),
+                "scx": (-1e-12, 1e-12),
+                "she": (-1e-12, 1e-12),
+                "cc": (-1e-12, 1e-12),
+                "xh": (-1e-12, 1e-12),
+                "yh": (-1e-12, 1e-12),
+            },
+            intrinsic_max_nfev=20,
+        )
+
+        self.assertIn(summary["accepted_stage"], {"baseline", "pose"})
+        self.assertLessEqual(
+            summary["final_reprojection_rms"], summary["baseline_reprojection_rms"]
+        )
+        self.assertLessEqual(
+            summary["final_mean_ray_convergence"],
+            summary["baseline_mean_ray_convergence"],
+        )
+        np.testing.assert_allclose(
+            reprojection_rms(observed_pixels, final_points, final_cals, control),
+            summary["final_reprojection_rms"],
+        )
+
+    def test_guarded_two_step_bundle_adjustment_preserves_pose_when_intrinsics_are_tight(
+        self,
+    ):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+                [0.0, 0.0, 3.0],
+                [6.0, -4.0, -2.0],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        start_cals = self.perturb_calibrations(self.true_cals)
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+        final_cals, final_points, summary = guarded_two_step_bundle_adjustment(
+            observed_pixels,
+            start_cals,
+            self.control,
+            OrientPar(),
+            intrinsics,
+            point_init=points,
+            fixed_camera_indices=[0, 1],
+            pose_prior_sigmas={
+                "x0": 1.0,
+                "y0": 1.0,
+                "z0": 1.0,
+                "omega": 0.01,
+                "phi": 0.01,
+                "kappa": 0.01,
+            },
+            pose_max_nfev=50,
+            intrinsic_prior_sigmas={
+                "k1": 1e-12,
+                "k2": 1e-12,
+                "k3": 1e-12,
+                "p1": 1e-12,
+                "p2": 1e-12,
+                "scx": 1e-12,
+                "she": 1e-12,
+                "cc": 1e-12,
+                "xh": 1e-12,
+                "yh": 1e-12,
+            },
+            intrinsic_parameter_bounds={
+                "k1": (-1e-10, 1e-10),
+                "k2": (-1e-10, 1e-10),
+                "k3": (-1e-10, 1e-10),
+                "p1": (-1e-10, 1e-10),
+                "p2": (-1e-10, 1e-10),
+                "scx": (-1e-12, 1e-12),
+                "she": (-1e-12, 1e-12),
+                "cc": (-1e-12, 1e-12),
+                "xh": (-1e-12, 1e-12),
+                "yh": (-1e-12, 1e-12),
+            },
+            intrinsic_max_nfev=20,
+        )
+
+        self.assertIn(summary["accepted_stage"], {"pose", "intrinsics"})
+        self.assertLess(
+            summary["final_reprojection_rms"], summary["baseline_reprojection_rms"]
+        )
+        self.assertLessEqual(
+            mean_ray_convergence(observed_pixels, final_cals, self.control),
+            summary["baseline_mean_ray_convergence"],
+        )
+        self.assertEqual(final_points.shape, points.shape)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_calibration_compare.py b/tests/test_calibration_compare.py
index ff5dfc9..7ef910f 100644
--- a/tests/test_calibration_compare.py
+++ b/tests/test_calibration_compare.py
@@ -61,4 +61,4 @@ def test_compare_modified_folder_reports_numeric_deltas(self):
 
 
 if __name__ == "__main__":
-    unittest.main()
\ No newline at end of file
+    unittest.main()
diff --git a/uv.lock b/uv.lock
index 6cb65f9..fd02693 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,1093 +1,1093 @@
-version = 1
-revision = 3
 requires-python = ">=3.14"
+revision = 3
+version = 1
 
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-version = "0.0.5"
-source = { registry = "https://pypi.org/simple" }
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-    { name = "pygments" },
+  {name = "pygments"}
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-sdist = { url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = "2024-05-10T11:23:10.216Z" }
+name = "accessible-pygments"
+sdist = {url = "https://files.pythonhosted.org/packages/bc/c1/bbac6a50d02774f91572938964c582fff4270eee73ab822a4aeea4d8b11b/accessible_pygments-0.0.5.tar.gz", hash = "sha256:40918d3e6a2b619ad424cb91e556bd3bd8865443d9f22f1dcdf79e33c8046872", size = 1377899, upload-time = "2024-05-10T11:23:10.216Z"}
+source = {registry = "https://pypi.org/simple"}
+version = "0.0.5"
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+  {url = "https://files.pythonhosted.org/packages/8d/3f/95338030883d8c8b91223b4e21744b04d11b161a3ef117295d8241f50ab4/accessible_pygments-0.0.5-py3-none-any.whl", hash = "sha256:88ae3211e68a1d0b011504b2ffc1691feafce124b845bd072ab6f9f66f34d4b7", size = 1395903, upload-time = "2024-05-10T11:23:08.421Z"}
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 [[package]]
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+source = {registry = "https://pypi.org/simple"}
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+  {url = "https://files.pythonhosted.org/packages/7e/b3/6b4067be973ae96ba0d615946e314c5ae35f9f993eca561b356540bb0c2b/alabaster-1.0.0-py3-none-any.whl", hash = "sha256:fc6786402dc3fcb2de3cabd5fe455a2db534b371124f1f21de8731783dec828b", size = 13929, upload-time = "2024-07-26T18:15:02.05Z"}
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-version = "4.12.1"
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-    { name = "idna" },
+  {name = "idna"}
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+name = "anyio"
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From 9b3bcc18bb8cb9c8d420a02c98b2f44382dd9622 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Fri, 6 Mar 2026 22:21:58 +0200
Subject: [PATCH 07/27] added bundle_adjustment

---
 docs/bundle_adjustment.md                | 241 ++++++++++
 docs/conf.py                             |   4 +-
 docs/index.md                            |   2 +-
 mypy.ini                                 |   1 +
 openptv_python/demo_bundle_adjustment.py | 573 +++++++++++++++++++++++
 openptv_python/orientation.py            | 130 ++++-
 tests/test_bundle_adjustment.py          | 160 ++++---
 7 files changed, 1018 insertions(+), 93 deletions(-)
 create mode 100644 docs/bundle_adjustment.md
 create mode 100644 openptv_python/demo_bundle_adjustment.py

diff --git a/docs/bundle_adjustment.md b/docs/bundle_adjustment.md
new file mode 100644
index 0000000..7c2d2fe
--- /dev/null
+++ b/docs/bundle_adjustment.md
@@ -0,0 +1,241 @@
+# Bundle Adjustment
+
+This page documents the bundle-adjustment routines implemented in `openptv_python.orientation` and the demo driver in `openptv_python.demo_bundle_adjustment`.
+
+## Overview
+
+The repository currently exposes two related calibration-refinement workflows:
+
+1. `multi_camera_bundle_adjustment`: jointly refine camera parameters and optionally 3D points by minimizing reprojection error.
+2. `guarded_two_step_bundle_adjustment`: run a pose-focused bundle-adjustment stage first, then a tightly constrained intrinsic stage, and reject the second stage if it makes the solution worse.
+
+The demo module compares several configurations on a case folder such as `tests/testing_fodder/test_cavity` and can write one updated calibration folder per experiment.
+
+## Case Layout
+
+The demo expects a case folder with this structure:
+
+```text
+case_dir/
+  cal/
+    cam1.tif.ori
+    cam1.tif.addpar
+    ...
+  parameters/
+    ptv.par
+    sequence.par
+  res_orig/
+    rt_is.*
+  img_orig/
+    cam1.%05d_targets
+    ...
+```
+
+This is the same layout used by `tests/testing_fodder/test_cavity`.
+
+## Objective Function
+
+For each observed 2D image point $u_{ij} = (x_{ij}, y_{ij})$ from camera $j$ and 3D point $X_i$, bundle adjustment minimizes reprojection residuals of the form
+
+$$
+r_{ij} =
+\begin{bmatrix}
+\hat{x}_{ij}(X_i, \theta_j) - x_{ij} \\
+\hat{y}_{ij}(X_i, \theta_j) - y_{ij}
+\end{bmatrix},
+$$
+
+where $\theta_j$ denotes the active calibration parameters for camera $j$.
+
+The solver minimizes a robust or linear least-squares objective
+
+$$
+\min_{\Theta, X}
+\sum_{i,j} \rho\left(\left\|W r_{ij}\right\|^2\right)
++ \sum_k \left(\frac{p_k - p_{k,0}}{\sigma_k}\right)^2,
+$$
+
+with:
+
+1. $W$ converting pixel residuals into normalized units using `pix_x` and `pix_y`.
+2. $\rho(\cdot)$ chosen by the `loss` argument, for example `linear` or `soft_l1`.
+3. Optional Gaussian-style priors on selected parameters via `prior_sigmas`.
+
+The implementation evaluates the forward projection through OpenPTV's existing camera and multimedia model, so refraction and the Brown-affine distortion model remain part of the optimization.
+
+## Implemented Algorithms
+
+## `multi_camera_bundle_adjustment`
+
+This is the general solver. It can optimize:
+
+1. Exterior parameters: `x0`, `y0`, `z0`, `omega`, `phi`, `kappa`.
+2. Optional intrinsic and distortion parameters controlled by `OrientPar` flags.
+3. The 3D points themselves, if `optimize_points=True`.
+
+Important implementation details:
+
+1. It uses `scipy.optimize.least_squares`.
+2. For `trf` and `dogbox`, the code supplies a Jacobian sparsity pattern so finite differencing scales with the true bundle-adjustment dependency graph instead of a dense matrix.
+3. Residuals are assembled camera-by-camera to reduce Python overhead.
+4. The function guards against scale ambiguity when points and camera poses are both free but too few cameras are fixed.
+
+In practice, the free variables are partitioned as:
+
+$$
+z = [\theta_{j_1}, \theta_{j_2}, \dots, X_1, X_2, \dots].
+$$
+
+Each observation residual depends only on:
+
+1. One camera parameter block.
+2. One 3D point block.
+
+That sparse structure is the reason `jac_sparsity` matters so much for runtime.
+
+## `guarded_two_step_bundle_adjustment`
+
+This routine is designed for a more conservative refinement flow:
+
+1. Start from baseline calibrations and points.
+2. Run a pose-oriented bundle-adjustment stage.
+3. Run a second intrinsic-focused stage with all cameras fixed in pose.
+4. Accept the intrinsic stage only if it does not degrade reprojection RMS and, by default, does not worsen mean ray convergence.
+
+This gives three possible final outcomes:
+
+1. `baseline`: both optimized stages are rejected.
+2. `pose`: the pose stage is accepted but the intrinsic stage is rejected.
+3. `intrinsics`: both stages are accepted.
+
+This is useful when the intrinsic update is intentionally tiny and should only be kept if it is clearly beneficial.
+
+## Metrics Reported
+
+The current routines report and the demo prints:
+
+1. `initial_reprojection_rms` and `final_reprojection_rms`.
+2. Per-camera reprojection RMS for `multi_camera_bundle_adjustment`.
+3. `baseline_mean_ray_convergence`, `pose_mean_ray_convergence`, and `final_mean_ray_convergence` for guarded runs.
+4. Runtime in seconds for each experiment.
+
+Reprojection RMS answers "how well do the updated cameras explain the observed image measurements?"
+
+Mean ray convergence answers "how tightly do back-projected camera rays meet in 3D?"
+
+Lower is better for both metrics.
+
+## Demo Script
+
+The demo entry point is:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment
+```
+
+By default it uses `tests/testing_fodder/test_cavity`, writes results into `tmp/bundle_adjustment_demo`, and evaluates several presets.
+
+### Demo Options
+
+The command-line options are:
+
+1. `case_dir`: optional positional argument pointing at a compatible case folder.
+2. `--max-frames N`: only load the first `N` frames from `sequence.par`.
+3. `--max-points-per-frame N`: only keep the first `N` fully observed points per frame.
+4. `--perturbation-scale S`: scale the deterministic starting pose perturbation.
+5. `--output-dir PATH`: write one output case folder per experiment under this directory.
+6. `--skip-write`: run the experiments without writing updated calibration folders.
+
+### Included Demo Presets
+
+The current demo compares four presets:
+
+1. `pose_trf_linear`: pose-only bundle adjustment with `method="trf"` and `loss="linear"`.
+2. `pose_soft_l1`: pose-only bundle adjustment with robust `soft_l1` loss.
+3. `pose_fixed_points`: pose-only bundle adjustment with `optimize_points=False`.
+4. `guarded_two_step`: pose stage plus tightly constrained intrinsic stage.
+
+### Example Commands
+
+Small, fast smoke test:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment \
+  --max-frames 1 \
+  --max-points-per-frame 16 \
+  --output-dir .tmp/demo_bundle_adjustment_check
+```
+
+Larger comparison on `test_cavity`:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment \
+  tests/testing_fodder/test_cavity \
+  --max-frames 2 \
+  --max-points-per-frame 80 \
+  --output-dir .tmp/demo_bundle_adjustment_runs
+```
+
+Run without writing calibration folders:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment --skip-write
+```
+
+## Output Folders
+
+When writing is enabled, each experiment produces a case copy like:
+
+```text
+tmp/bundle_adjustment_demo/
+  pose_trf_linear/
+    cal/
+      cam1.tif.ori
+      cam1.tif.addpar
+      ...
+    calibration_delta.txt
+```
+
+`calibration_delta.txt` is generated with `openptv_python.calibration_compare` and shows camera-by-camera parameter differences relative to the source case.
+
+## Choosing Options
+
+These are the main tradeoffs when selecting settings.
+
+### Loss Function
+
+1. `linear`: use when correspondences are trusted and you want a pure least-squares solution.
+2. `soft_l1`: use when some observations may behave like outliers.
+
+### Optimizing Points
+
+1. `optimize_points=True`: stronger joint refinement, but more variables and slower solves.
+2. `optimize_points=False`: faster, more stable if initial 3D points are already good enough.
+
+### Fixed Cameras
+
+Fixing multiple cameras helps remove similarity-gauge ambiguity. If both points and poses are free, the implementation requires either:
+
+1. At least two fixed cameras.
+2. Or translation priors for `x0`, `y0`, and `z0`.
+
+### Priors and Bounds
+
+1. `prior_sigmas` softly regularize motion away from the starting calibration.
+2. `parameter_bounds` clip the allowed movement relative to the starting calibration.
+
+This is especially useful for guarded intrinsic updates where you only want very small corrections.
+
+## Practical Notes
+
+1. `lm` can be effective on small synthetic problems, but `trf` is generally the better default for larger real cases because it supports bounds and Jacobian sparsity.
+2. Real-case runtime depends strongly on the number of frames and points included.
+3. `read_targets` currently prints filenames as it loads target files, so demo output includes those lines.
+
+## Related Code
+
+The main implementation lives in:
+
+1. `openptv_python.orientation`
+2. `openptv_python.demo_bundle_adjustment`
+3. `openptv_python.calibration_compare`
diff --git a/docs/conf.py b/docs/conf.py
index 6673d7d..5b1ba6f 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -27,12 +27,14 @@
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
 extensions = [
-    "autoapi.extension",
     "myst_parser",
     "sphinx.ext.autodoc",
     "sphinx.ext.napoleon",
 ]
 
+if os.environ.get("OPENPTV_ENABLE_AUTOAPI") == "1":
+    extensions.append("autoapi.extension")
+
 # autodoc configuration
 autodoc_typehints = "none"
 
diff --git a/docs/index.md b/docs/index.md
index 6feb5cd..37ea85c 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -7,7 +7,7 @@ Python version of the OpenPTV library.
 :maxdepth: 2
 
 README.md
-API Reference <_api/openptv_python/index>
+bundle_adjustment.md
 ```
 
 # Indices and tables
diff --git a/mypy.ini b/mypy.ini
index e5d1ae2..1f68091 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -1,5 +1,6 @@
 [mypy]
 ignore_missing_imports = True
+exclude = (^build/|^docs/_build/)
 ; disallow_untyped_defs = True
 ; disallow_incomplete_defs = True
 ; check_untyped_defs = True
diff --git a/openptv_python/demo_bundle_adjustment.py b/openptv_python/demo_bundle_adjustment.py
new file mode 100644
index 0000000..4fed2db
--- /dev/null
+++ b/openptv_python/demo_bundle_adjustment.py
@@ -0,0 +1,573 @@
+"""Command-line demo for bundle-adjustment experiments on OpenPTV cases."""
+
+from __future__ import annotations
+
+import argparse
+import shutil
+from dataclasses import dataclass
+from pathlib import Path
+from time import perf_counter
+from typing import Dict, Iterable, List, cast
+
+import numpy as np
+
+from .calibration import Calibration, read_calibration, write_calibration
+from .calibration_compare import compare_calibration_folders, format_calibration_comparison
+from .orientation import (
+    guarded_two_step_bundle_adjustment,
+    mean_ray_convergence,
+    multi_camera_bundle_adjustment,
+    reprojection_rms,
+)
+from .parameters import ControlPar, OrientPar, SequencePar
+from .tracking_frame_buf import read_path_frame, read_targets
+
+
+@dataclass(frozen=True)
+class ExperimentSpec:
+    """Configuration for one bundle-adjustment demo run."""
+
+    name: str
+    description: str
+    mode: str
+    ba_kwargs: Dict[str, object]
+
+
+@dataclass
+class ExperimentResult:
+    """Collected metrics for one bundle-adjustment demo run."""
+
+    name: str
+    description: str
+    duration_sec: float
+    success: bool
+    initial_rms: float
+    final_rms: float
+    baseline_ray_convergence: float
+    final_ray_convergence: float
+    notes: str
+    cal_dir: Path | None
+
+
+def clone_calibration(cal: Calibration) -> Calibration:
+    """Return a detached copy of a calibration object."""
+    return Calibration(
+        ext_par=cal.ext_par.copy(),
+        int_par=cal.int_par.copy(),
+        glass_par=cal.glass_par.copy(),
+        added_par=cal.added_par.copy(),
+        mmlut=cal.mmlut,
+        mmlut_data=cal.mmlut_data,
+    )
+
+
+def perturb_calibrations(cals: List[Calibration], scale: float) -> List[Calibration]:
+    """Apply deterministic pose perturbations so BA has something to recover."""
+    perturbed = [clone_calibration(cal) for cal in cals]
+    deltas = [
+        (np.array([0.0, 0.0, 0.0]), np.array([0.0, 0.0, 0.0])),
+        (np.array([0.5, -0.3, 0.2]), np.array([0.004, -0.003, 0.002])),
+        (np.array([-0.4, 0.3, -0.2]), np.array([-0.003, 0.003, -0.002])),
+        (np.array([0.3, 0.4, -0.2]), np.array([0.003, 0.002, -0.002])),
+    ]
+    for cal, (pos_delta, angle_delta) in zip(perturbed, deltas):
+        cal.set_pos(cal.get_pos() + pos_delta * scale)
+        cal.set_angles(cal.get_angles() + angle_delta * scale)
+    return perturbed
+
+
+def discover_num_cams(cal_dir: Path) -> int:
+    """Infer the number of cameras from calibration .ori files."""
+    return len(sorted(cal_dir.glob("*.ori")))
+
+
+def load_calibrations(case_dir: Path, num_cams: int) -> List[Calibration]:
+    """Load all camera calibrations from a case folder."""
+    cal_dir = case_dir / "cal"
+    return [
+        read_calibration(
+            cal_dir / f"cam{cam_num}.tif.ori",
+            cal_dir / f"cam{cam_num}.tif.addpar",
+        )
+        for cam_num in range(1, num_cams + 1)
+    ]
+
+
+def load_case_observations(
+    case_dir: Path,
+    num_cams: int,
+    *,
+    max_frames: int | None,
+    max_points_per_frame: int | None,
+) -> tuple[ControlPar, np.ndarray, np.ndarray]:
+    """Load quadruplet observations and initial 3D points from a case folder."""
+    control = ControlPar(num_cams).from_file(case_dir / "parameters/ptv.par")
+    seq = SequencePar.from_file(case_dir / "parameters/sequence.par", num_cams)
+
+    observed_batches = []
+    point_batches = []
+    frames = list(range(seq.first, seq.last + 1))
+    if max_frames is not None:
+        frames = frames[:max_frames]
+
+    for frame in frames:
+        cor_buf, path_buf = read_path_frame(
+            str(case_dir / "res_orig/rt_is"),
+            "",
+            "",
+            frame,
+        )
+        targets = [
+            read_targets(str(case_dir / f"img_orig/cam{cam_num}.%05d"), frame)
+            for cam_num in range(1, num_cams + 1)
+        ]
+        subset = [
+            pt_num
+            for pt_num, corres in enumerate(cor_buf)
+            if np.all(corres.p[:num_cams] >= 0)
+        ]
+        if max_points_per_frame is not None:
+            subset = subset[:max_points_per_frame]
+
+        observed_pixels = np.full((len(subset), num_cams, 2), np.nan, dtype=float)
+        point_init = np.empty((len(subset), 3), dtype=float)
+        for out_num, pt_num in enumerate(subset):
+            point_init[out_num] = path_buf[pt_num].x
+            for cam in range(num_cams):
+                target_index = cor_buf[pt_num].p[cam]
+                observed_pixels[out_num, cam, 0] = targets[cam][target_index].x
+                observed_pixels[out_num, cam, 1] = targets[cam][target_index].y
+
+        observed_batches.append(observed_pixels)
+        point_batches.append(point_init)
+
+    if not observed_batches:
+        raise ValueError(f"No observations loaded from {case_dir}")
+
+    return (
+        control,
+        np.concatenate(observed_batches, axis=0),
+        np.concatenate(point_batches, axis=0),
+    )
+
+
+def default_experiments() -> List[ExperimentSpec]:
+    """Return a set of representative BA configurations."""
+    pose_priors = {
+        "x0": 0.5,
+        "y0": 0.5,
+        "z0": 0.5,
+        "omega": 0.005,
+        "phi": 0.005,
+        "kappa": 0.005,
+    }
+    pose_bounds = {
+        "x0": (-2.0, 2.0),
+        "y0": (-2.0, 2.0),
+        "z0": (-2.0, 2.0),
+        "omega": (-0.02, 0.02),
+        "phi": (-0.02, 0.02),
+        "kappa": (-0.02, 0.02),
+    }
+    tight_intrinsic_priors = {
+        "k1": 1e-12,
+        "k2": 1e-12,
+        "k3": 1e-12,
+        "p1": 1e-12,
+        "p2": 1e-12,
+        "scx": 1e-12,
+        "she": 1e-12,
+        "cc": 1e-12,
+        "xh": 1e-12,
+        "yh": 1e-12,
+    }
+    tight_intrinsic_bounds = {
+        "k1": (-1e-10, 1e-10),
+        "k2": (-1e-10, 1e-10),
+        "k3": (-1e-10, 1e-10),
+        "p1": (-1e-10, 1e-10),
+        "p2": (-1e-10, 1e-10),
+        "scx": (-1e-12, 1e-12),
+        "she": (-1e-12, 1e-12),
+        "cc": (-1e-12, 1e-12),
+        "xh": (-1e-12, 1e-12),
+        "yh": (-1e-12, 1e-12),
+    }
+
+    intrinsics = OrientPar()
+    intrinsics.k1flag = 1
+    intrinsics.p1flag = 1
+    intrinsics.p2flag = 1
+
+    return [
+        ExperimentSpec(
+            name="pose_trf_linear",
+            description="Pose-only BA with linear loss and TRF",
+            mode="multi",
+            ba_kwargs={
+                "orient_par": OrientPar(),
+                "fixed_camera_indices": [0, 1],
+                "loss": "linear",
+                "method": "trf",
+                "prior_sigmas": pose_priors,
+                "parameter_bounds": pose_bounds,
+                "max_nfev": 12,
+            },
+        ),
+        ExperimentSpec(
+            name="pose_soft_l1",
+            description="Pose-only BA with robust soft_l1 loss",
+            mode="multi",
+            ba_kwargs={
+                "orient_par": OrientPar(),
+                "fixed_camera_indices": [0, 1],
+                "loss": "soft_l1",
+                "f_scale": 1.0,
+                "method": "trf",
+                "prior_sigmas": pose_priors,
+                "parameter_bounds": pose_bounds,
+                "max_nfev": 12,
+            },
+        ),
+        ExperimentSpec(
+            name="pose_fixed_points",
+            description="Pose-only BA with fixed 3D points",
+            mode="multi",
+            ba_kwargs={
+                "orient_par": OrientPar(),
+                "fixed_camera_indices": [0, 1],
+                "loss": "linear",
+                "method": "trf",
+                "prior_sigmas": pose_priors,
+                "parameter_bounds": pose_bounds,
+                "max_nfev": 12,
+                "optimize_points": False,
+            },
+        ),
+        ExperimentSpec(
+            name="guarded_two_step",
+            description="Pose stage followed by tightly constrained intrinsic stage",
+            mode="guarded",
+            ba_kwargs={
+                "pose_orient_par": OrientPar(),
+                "intrinsic_orient_par": intrinsics,
+                "fixed_camera_indices": [0, 1],
+                "pose_prior_sigmas": pose_priors,
+                "pose_parameter_bounds": pose_bounds,
+                "pose_max_nfev": 8,
+                "intrinsic_prior_sigmas": tight_intrinsic_priors,
+                "intrinsic_parameter_bounds": tight_intrinsic_bounds,
+                "intrinsic_max_nfev": 4,
+            },
+        ),
+    ]
+
+
+def ensure_output_case_layout(source_case_dir: Path, output_case_dir: Path) -> Path:
+    """Copy the source case and return the writable calibration directory."""
+    shutil.copytree(source_case_dir, output_case_dir, dirs_exist_ok=True)
+    cal_dir = output_case_dir / "cal"
+    cal_dir.mkdir(parents=True, exist_ok=True)
+    return cal_dir
+
+
+def write_calibration_folder(cals: List[Calibration], cal_dir: Path) -> None:
+    """Write one calibration folder using OpenPTV naming conventions."""
+    cal_dir.mkdir(parents=True, exist_ok=True)
+    for cam_num, cal in enumerate(cals, start=1):
+        stem = f"cam{cam_num}.tif"
+        write_calibration(cal, cal_dir / f"{stem}.ori", cal_dir / f"{stem}.addpar")
+
+
+def run_experiment(
+    spec: ExperimentSpec,
+    *,
+    observed_pixels: np.ndarray,
+    point_init: np.ndarray,
+    control: ControlPar,
+    start_cals: List[Calibration],
+    source_case_dir: Path,
+    output_dir: Path | None,
+) -> ExperimentResult:
+    """Execute one experiment and collect metrics and optional outputs."""
+    working_cals = [clone_calibration(cal) for cal in start_cals]
+    baseline_rms = reprojection_rms(observed_pixels, point_init, working_cals, control)
+    baseline_ray = mean_ray_convergence(observed_pixels, working_cals, control)
+
+    start = perf_counter()
+    notes = ""
+    if spec.mode == "multi":
+        orient_par = cast(OrientPar, spec.ba_kwargs["orient_par"])
+        refined_cals, refined_points, result = multi_camera_bundle_adjustment(
+            observed_pixels,
+            working_cals,
+            control,
+            orient_par,
+            point_init=point_init,
+            fix_first_camera=cast(bool, spec.ba_kwargs.get("fix_first_camera", True)),
+            fixed_camera_indices=cast(
+                List[int] | None,
+                spec.ba_kwargs.get("fixed_camera_indices"),
+            ),
+            loss=cast(str, spec.ba_kwargs.get("loss", "soft_l1")),
+            f_scale=cast(float, spec.ba_kwargs.get("f_scale", 1.0)),
+            method=cast(str, spec.ba_kwargs.get("method", "trf")),
+            prior_sigmas=cast(
+                Dict[str, float] | None,
+                spec.ba_kwargs.get("prior_sigmas"),
+            ),
+            parameter_bounds=cast(
+                Dict[str, tuple[float, float]] | None,
+                spec.ba_kwargs.get("parameter_bounds"),
+            ),
+            max_nfev=cast(int | None, spec.ba_kwargs.get("max_nfev")),
+            optimize_extrinsics=cast(
+                bool,
+                spec.ba_kwargs.get("optimize_extrinsics", True),
+            ),
+            optimize_points=cast(bool, spec.ba_kwargs.get("optimize_points", True)),
+            ftol=cast(float | None, spec.ba_kwargs.get("ftol")),
+            xtol=cast(float | None, spec.ba_kwargs.get("xtol")),
+            gtol=cast(float | None, spec.ba_kwargs.get("gtol")),
+        )
+        success = bool(result.success)
+        final_rms = float(result["final_reprojection_rms"])
+        final_ray = mean_ray_convergence(observed_pixels, refined_cals, control)
+        notes = str(result.message)
+    elif spec.mode == "guarded":
+        pose_orient_par = cast(OrientPar, spec.ba_kwargs["pose_orient_par"])
+        intrinsic_orient_par = cast(OrientPar, spec.ba_kwargs["intrinsic_orient_par"])
+        refined_cals, refined_points, summary = guarded_two_step_bundle_adjustment(
+            observed_pixels,
+            working_cals,
+            control,
+            pose_orient_par,
+            intrinsic_orient_par,
+            point_init=point_init,
+            fixed_camera_indices=cast(
+                List[int] | None,
+                spec.ba_kwargs.get("fixed_camera_indices"),
+            ),
+            pose_prior_sigmas=cast(
+                Dict[str, float] | None,
+                spec.ba_kwargs.get("pose_prior_sigmas"),
+            ),
+            pose_parameter_bounds=cast(
+                Dict[str, tuple[float, float]] | None,
+                spec.ba_kwargs.get("pose_parameter_bounds"),
+            ),
+            pose_loss=cast(str, spec.ba_kwargs.get("pose_loss", "linear")),
+            pose_method=cast(str, spec.ba_kwargs.get("pose_method", "trf")),
+            pose_max_nfev=cast(int | None, spec.ba_kwargs.get("pose_max_nfev")),
+            intrinsic_prior_sigmas=cast(
+                Dict[str, float] | None,
+                spec.ba_kwargs.get("intrinsic_prior_sigmas"),
+            ),
+            intrinsic_parameter_bounds=cast(
+                Dict[str, tuple[float, float]] | None,
+                spec.ba_kwargs.get("intrinsic_parameter_bounds"),
+            ),
+            intrinsic_loss=cast(
+                str,
+                spec.ba_kwargs.get("intrinsic_loss", "linear"),
+            ),
+            intrinsic_method=cast(
+                str,
+                spec.ba_kwargs.get("intrinsic_method", "trf"),
+            ),
+            intrinsic_max_nfev=cast(
+                int | None,
+                spec.ba_kwargs.get("intrinsic_max_nfev"),
+            ),
+            intrinsic_ftol=cast(float | None, spec.ba_kwargs.get("intrinsic_ftol", 1e-12)),
+            intrinsic_xtol=cast(float | None, spec.ba_kwargs.get("intrinsic_xtol", 1e-12)),
+            intrinsic_gtol=cast(float | None, spec.ba_kwargs.get("intrinsic_gtol", 1e-12)),
+            pose_optimize_points=cast(
+                bool,
+                spec.ba_kwargs.get("pose_optimize_points", True),
+            ),
+            intrinsic_optimize_points=cast(
+                bool,
+                spec.ba_kwargs.get("intrinsic_optimize_points", True),
+            ),
+            reject_worse_solution=cast(
+                bool,
+                spec.ba_kwargs.get("reject_worse_solution", True),
+            ),
+            reject_on_ray_convergence=cast(
+                bool,
+                spec.ba_kwargs.get("reject_on_ray_convergence", True),
+            ),
+        )
+        success = True
+        final_rms = cast(float, summary["final_reprojection_rms"])
+        final_ray = cast(float, summary["final_mean_ray_convergence"])
+        notes = f"accepted_stage={summary['accepted_stage']}"
+    else:
+        raise ValueError(f"Unknown experiment mode: {spec.mode}")
+
+    duration_sec = perf_counter() - start
+
+    cal_dir = None
+    if output_dir is not None:
+        case_out_dir = output_dir / spec.name
+        cal_dir = ensure_output_case_layout(source_case_dir, case_out_dir)
+        write_calibration_folder(refined_cals, cal_dir)
+        comparison = compare_calibration_folders(source_case_dir / "cal", cal_dir)
+        (case_out_dir / "calibration_delta.txt").write_text(
+            format_calibration_comparison(
+                comparison,
+                reference_dir=source_case_dir / "cal",
+                candidate_dir=cal_dir,
+            )
+            + "\n",
+            encoding="utf-8",
+        )
+
+    return ExperimentResult(
+        name=spec.name,
+        description=spec.description,
+        duration_sec=duration_sec,
+        success=success,
+        initial_rms=baseline_rms,
+        final_rms=final_rms,
+        baseline_ray_convergence=baseline_ray,
+        final_ray_convergence=final_ray,
+        notes=notes,
+        cal_dir=cal_dir,
+    )
+
+
+def format_results(results: Iterable[ExperimentResult]) -> str:
+    """Render a compact plain-text summary table."""
+    rows = list(results)
+    headers = (
+        "name",
+        "success",
+        "seconds",
+        "rms_before",
+        "rms_after",
+        "ray_before",
+        "ray_after",
+        "notes",
+    )
+    data = [
+        [
+            row.name,
+            "yes" if row.success else "no",
+            f"{row.duration_sec:.2f}",
+            f"{row.initial_rms:.6f}",
+            f"{row.final_rms:.6f}",
+            f"{row.baseline_ray_convergence:.6f}",
+            f"{row.final_ray_convergence:.6f}",
+            row.notes,
+        ]
+        for row in rows
+    ]
+    widths = [len(header) for header in headers]
+    for row in data:
+        for index, value in enumerate(row):
+            widths[index] = max(widths[index], len(value))
+
+    def render_row(values: List[str]) -> str:
+        return "  ".join(value.ljust(widths[index]) for index, value in enumerate(values))
+
+    separator = "  ".join("-" * width for width in widths)
+    lines = [render_row(list(headers)), separator]
+    lines.extend(render_row(row) for row in data)
+    return "\n".join(lines)
+
+
+def parse_args(argv: Iterable[str] | None = None) -> argparse.Namespace:
+    """Parse command-line arguments for the BA demo."""
+    parser = argparse.ArgumentParser(
+        description="Demonstrate bundle-adjustment options on test_cavity or another compatible case.",
+    )
+    parser.add_argument(
+        "case_dir",
+        type=Path,
+        nargs="?",
+        default=Path("tests/testing_fodder/test_cavity"),
+        help="Case folder containing cal/, parameters/, res_orig/, and img_orig/.",
+    )
+    parser.add_argument(
+        "--max-frames",
+        type=int,
+        default=None,
+        help="Only use the first N frames from sequence.par.",
+    )
+    parser.add_argument(
+        "--max-points-per-frame",
+        type=int,
+        default=80,
+        help="Only use the first N fully observed points from each frame.",
+    )
+    parser.add_argument(
+        "--perturbation-scale",
+        type=float,
+        default=1.0,
+        help="Scale factor for the deterministic starting-calibration perturbation.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        type=Path,
+        default=Path("tmp/bundle_adjustment_demo"),
+        help="Where to write one output case folder per experiment.",
+    )
+    parser.add_argument(
+        "--skip-write",
+        action="store_true",
+        help="Run comparisons but do not write updated calibration folders.",
+    )
+    return parser.parse_args(list(argv) if argv is not None else None)
+
+
+def main(argv: Iterable[str] | None = None) -> int:
+    """Run the bundle-adjustment demo."""
+    args = parse_args(argv)
+    case_dir = args.case_dir.resolve()
+    num_cams = discover_num_cams(case_dir / "cal")
+
+    control, observed_pixels, point_init = load_case_observations(
+        case_dir,
+        num_cams,
+        max_frames=args.max_frames,
+        max_points_per_frame=args.max_points_per_frame,
+    )
+    true_cals = load_calibrations(case_dir, num_cams)
+    start_cals = perturb_calibrations(true_cals, args.perturbation_scale)
+
+    output_dir = None if args.skip_write else args.output_dir.resolve()
+    if output_dir is not None:
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+    print(f"Case: {case_dir}")
+    print(f"Cameras: {num_cams}")
+    print(f"Observations: {observed_pixels.shape[0]} points across {observed_pixels.shape[1]} cameras")
+    print(f"Output folders: {output_dir if output_dir is not None else 'disabled'}")
+    print()
+
+    results = []
+    for spec in default_experiments():
+        print(f"Running {spec.name}: {spec.description}")
+        result = run_experiment(
+            spec,
+            observed_pixels=observed_pixels,
+            point_init=point_init,
+            control=control,
+            start_cals=start_cals,
+            source_case_dir=case_dir,
+            output_dir=output_dir,
+        )
+        results.append(result)
+        if result.cal_dir is not None:
+            print(f"  wrote calibration folder: {result.cal_dir}")
+        print(f"  final RMS: {result.final_rms:.6f} px in {result.duration_sec:.2f} s")
+        print()
+
+    print(format_results(results))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
\ No newline at end of file
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index 1f914df..b86c0ad 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -11,14 +11,20 @@
 from .calibration import Calibration
 from .constants import CONVERGENCE, IDT, NPAR, NUM_ITER, POS_INF
 from .epi import epi_mm_2D
-from .imgcoord import img_coord
+from .imgcoord import image_coordinates, img_coord
 
 # from .lsqadj import ata, atl, matinv, matmul
 from .parameters import ControlPar, MultimediaPar, OrientPar, VolumePar
 from .ray_tracing import ray_tracing
 from .sortgrid import sortgrid
 from .tracking_frame_buf import Target
-from .trafo import correct_brown_affine, dist_to_flat, metric_to_pixel, pixel_to_metric
+from .trafo import (
+    arr_metric_to_pixel,
+    correct_brown_affine,
+    dist_to_flat,
+    metric_to_pixel,
+    pixel_to_metric,
+)
 from .vec_utils import unit_vector, vec_norm, vec_set
 
 
@@ -1355,6 +1361,54 @@ def _expand_parameter_limits(
     return np.asarray(lower, dtype=np.float64), np.asarray(upper, dtype=np.float64)
 
 
+def _bundle_adjustment_jacobian_sparsity(
+    obs_mask: np.ndarray,
+    num_cams: int,
+    optimized_cam_indices: List[int],
+    camera_block_size: int,
+    point_offset: int,
+    optimize_points: bool,
+    active_priors: List[Tuple[int, int, float, float]],
+) -> scipy.sparse.csr_matrix:
+    """Build the Jacobian sparsity pattern for finite-difference bundle adjustment."""
+    num_observation_residuals = int(np.count_nonzero(obs_mask) * 2)
+    num_prior_residuals = len(active_priors)
+    total_residuals = num_observation_residuals + num_prior_residuals
+    total_parameters = point_offset + (obs_mask.shape[0] * 3 if optimize_points else 0)
+
+    sparsity = scipy.sparse.lil_matrix(
+        (total_residuals, total_parameters),
+        dtype=np.int8,
+    )
+    optimized_cam_lookup = {
+        cam: cam_index for cam_index, cam in enumerate(optimized_cam_indices)
+    }
+
+    row = 0
+    for point_index in range(obs_mask.shape[0]):
+        point_start = point_offset + point_index * 3
+        for cam in range(num_cams):
+            if not obs_mask[point_index, cam]:
+                continue
+
+            cam_index = optimized_cam_lookup.get(cam)
+            if cam_index is not None and camera_block_size > 0:
+                cam_start = cam_index * camera_block_size
+                sparsity[row : row + 2, cam_start : cam_start + camera_block_size] = 1
+
+            if optimize_points:
+                sparsity[row : row + 2, point_start : point_start + 3] = 1
+
+            row += 2
+
+    for cam_index, param_index, _sigma, _base_value in active_priors:
+        cam_start = cam_index * camera_block_size
+        sparsity[row, cam_start + param_index] = 1
+        row += 1
+
+    return sparsity.tocsr()
+
+
 def multi_camera_bundle_adjustment(
     observed_pixels: np.ndarray,
     cals: List[Calibration],
@@ -1482,6 +1536,20 @@ def multi_camera_bundle_adjustment(
         x0 = np.empty(0, dtype=np.float64)
 
     point_offset = camera_block_size * len(optimized_cam_indices)
+    obs_mask = np.all(np.isfinite(observed_pixels), axis=2)
+    observation_point_indices = [
+        np.flatnonzero(obs_mask[:, cam]) for cam in range(num_cams)
+    ]
+
+    active_priors = []
+    for cam_index, _cam in enumerate(optimized_cam_indices):
+        for param_index, (name, base_value) in enumerate(
+            zip(parameter_names, base_camera_blocks[cam_index])
+        ):
+            sigma = prior_sigmas.get(name)
+            if sigma is None or sigma <= 0:
+                continue
+            active_priors.append((cam_index, param_index, sigma, base_value))
 
     camera_lower, camera_upper = _expand_parameter_limits(
         parameter_names,
@@ -1524,30 +1592,30 @@ def unpack_parameters(params: np.ndarray) -> Tuple[List[Calibration], np.ndarray
 
     def residual_vector(params: np.ndarray) -> np.ndarray:
         trial_cals, points = unpack_parameters(params)
-        residuals = []
-        for pt in range(num_points):
-            for cam in range(num_cams):
-                obs = observed_pixels[pt, cam]
-                if not np.all(np.isfinite(obs)):
-                    continue
-                x_metric, y_metric = img_coord(points[pt], trial_cals[cam], cpar.mm)
-                proj_x, proj_y = metric_to_pixel(x_metric, y_metric, cpar)
-                residuals.append((proj_x - obs[0]) / cpar.pix_x)
-                residuals.append((proj_y - obs[1]) / cpar.pix_y)
-
-        if prior_sigmas:
-            offset = 0
-            for cam_index, cam in enumerate(optimized_cam_indices):
-                block = params[offset : offset + camera_block_size]
-                base_block = base_camera_blocks[cam_index]
-                for name, value, base_value in zip(parameter_names, block, base_block):
-                    sigma = prior_sigmas.get(name)
-                    if sigma is None or sigma <= 0:
-                        continue
-                    residuals.append((value - base_value) / sigma)
-                offset += camera_block_size
-
-        return np.asarray(residuals, dtype=np.float64)
+        residuals = np.empty(int(np.count_nonzero(obs_mask) * 2) + len(active_priors))
+        row = 0
+
+        for cam, point_indices in enumerate(observation_point_indices):
+            if point_indices.size == 0:
+                continue
+
+            projected_pixels = arr_metric_to_pixel(
+                image_coordinates(points[point_indices], trial_cals[cam], cpar.mm),
+                cpar,
+            )
+            observed = observed_pixels[point_indices, cam, :]
+            diffs = projected_pixels - observed
+            residual_count = point_indices.size * 2
+            residuals[row : row + residual_count : 2] = diffs[:, 0] / cpar.pix_x
+            residuals[row + 1 : row + residual_count : 2] = diffs[:, 1] / cpar.pix_y
+            row += residual_count
+
+        for cam_index, param_index, sigma, base_value in active_priors:
+            value = params[cam_index * camera_block_size + param_index]
+            residuals[row] = (value - base_value) / sigma
+            row += 1
+
+        return residuals
 
     initial_cals, initial_points = unpack_parameters(x0)
     initial_rms = reprojection_rms(observed_pixels, initial_points, initial_cals, cpar)
@@ -1562,6 +1630,16 @@ def residual_vector(params: np.ndarray) -> np.ndarray:
         "max_nfev": max_nfev,
         "bounds": bounds,
     }
+    if method != "lm" and x0.size > 0:
+        least_squares_kwargs["jac_sparsity"] = _bundle_adjustment_jacobian_sparsity(
+            obs_mask,
+            num_cams,
+            optimized_cam_indices,
+            camera_block_size,
+            point_offset,
+            optimize_points,
+            active_priors,
+        )
     if ftol is not None:
         least_squares_kwargs["ftol"] = ftol
     if xtol is not None:
diff --git a/tests/test_bundle_adjustment.py b/tests/test_bundle_adjustment.py
index cb84caf..2ca4d70 100644
--- a/tests/test_bundle_adjustment.py
+++ b/tests/test_bundle_adjustment.py
@@ -1,5 +1,6 @@
 import unittest
 from pathlib import Path
+from unittest.mock import patch
 
 import numpy as np
 
@@ -237,78 +238,107 @@ def test_bundle_adjustment_rejects_scale_ambiguous_configuration(self):
             )
 
     def test_guarded_two_step_bundle_adjustment_rejects_bad_intrinsics_stage(self):
-        cavity_dir = Path("tests/testing_fodder/test_cavity")
-        control = ControlPar(4).from_file(cavity_dir / "parameters/ptv.par")
-        true_cals = [
-            read_calibration(
-                cavity_dir / f"cal/cam{cam_num}.tif.ori",
-                cavity_dir / f"cal/cam{cam_num}.tif.addpar",
-            )
-            for cam_num in range(1, 5)
-        ]
-        observed_pixels, point_init = self.cavity_quadruplet_observations(
-            cavity_dir, control
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+                [0.0, 0.0, 3.0],
+                [6.0, -4.0, -2.0],
+            ],
+            dtype=float,
         )
-        start_cals = self.lightly_perturb_calibrations(true_cals)
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        start_cals = self.lightly_perturb_calibrations(self.true_cals)
+        pose_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        bad_intrinsic_cals = [self.clone_calibration(cal) for cal in pose_cals]
+        for cal in bad_intrinsic_cals[1:]:
+            cal.added_par[0] += 1e-3
+            cal.added_par[3] += 5e-4
+            cal.added_par[4] -= 5e-4
 
         intrinsics = OrientPar()
         intrinsics.k1flag = 1
         intrinsics.p1flag = 1
         intrinsics.p2flag = 1
 
-        final_cals, final_points, summary = guarded_two_step_bundle_adjustment(
-            observed_pixels,
-            start_cals,
-            control,
-            OrientPar(),
-            intrinsics,
-            point_init=point_init,
-            fixed_camera_indices=[0, 1],
-            pose_prior_sigmas={
-                "x0": 0.5,
-                "y0": 0.5,
-                "z0": 0.5,
-                "omega": 0.005,
-                "phi": 0.005,
-                "kappa": 0.005,
-            },
-            pose_parameter_bounds={
-                "x0": (-2.0, 2.0),
-                "y0": (-2.0, 2.0),
-                "z0": (-2.0, 2.0),
-                "omega": (-0.02, 0.02),
-                "phi": (-0.02, 0.02),
-                "kappa": (-0.02, 0.02),
-            },
-            pose_max_nfev=60,
-            intrinsic_prior_sigmas={
-                "k1": 1e-12,
-                "k2": 1e-12,
-                "k3": 1e-12,
-                "p1": 1e-12,
-                "p2": 1e-12,
-                "scx": 1e-12,
-                "she": 1e-12,
-                "cc": 1e-12,
-                "xh": 1e-12,
-                "yh": 1e-12,
-            },
-            intrinsic_parameter_bounds={
-                "k1": (-1e-10, 1e-10),
-                "k2": (-1e-10, 1e-10),
-                "k3": (-1e-10, 1e-10),
-                "p1": (-1e-10, 1e-10),
-                "p2": (-1e-10, 1e-10),
-                "scx": (-1e-12, 1e-12),
-                "she": (-1e-12, 1e-12),
-                "cc": (-1e-12, 1e-12),
-                "xh": (-1e-12, 1e-12),
-                "yh": (-1e-12, 1e-12),
-            },
-            intrinsic_max_nfev=20,
-        )
+        with patch(
+            "openptv_python.orientation.multi_camera_bundle_adjustment",
+            side_effect=[
+                (pose_cals, points.copy(), {"success": True, "stage": "pose"}),
+                (
+                    bad_intrinsic_cals,
+                    points.copy(),
+                    {"success": True, "stage": "intrinsics"},
+                ),
+            ],
+        ) as mocked_adjustment:
+            final_cals, final_points, summary = guarded_two_step_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                intrinsics,
+                point_init=points,
+                fixed_camera_indices=[0, 1],
+                pose_prior_sigmas={
+                    "x0": 0.5,
+                    "y0": 0.5,
+                    "z0": 0.5,
+                    "omega": 0.005,
+                    "phi": 0.005,
+                    "kappa": 0.005,
+                },
+                pose_parameter_bounds={
+                    "x0": (-2.0, 2.0),
+                    "y0": (-2.0, 2.0),
+                    "z0": (-2.0, 2.0),
+                    "omega": (-0.02, 0.02),
+                    "phi": (-0.02, 0.02),
+                    "kappa": (-0.02, 0.02),
+                },
+                pose_max_nfev=60,
+                intrinsic_prior_sigmas={
+                    "k1": 1e-12,
+                    "k2": 1e-12,
+                    "k3": 1e-12,
+                    "p1": 1e-12,
+                    "p2": 1e-12,
+                    "scx": 1e-12,
+                    "she": 1e-12,
+                    "cc": 1e-12,
+                    "xh": 1e-12,
+                    "yh": 1e-12,
+                },
+                intrinsic_parameter_bounds={
+                    "k1": (-1e-10, 1e-10),
+                    "k2": (-1e-10, 1e-10),
+                    "k3": (-1e-10, 1e-10),
+                    "p1": (-1e-10, 1e-10),
+                    "p2": (-1e-10, 1e-10),
+                    "scx": (-1e-12, 1e-12),
+                    "she": (-1e-12, 1e-12),
+                    "cc": (-1e-12, 1e-12),
+                    "xh": (-1e-12, 1e-12),
+                    "yh": (-1e-12, 1e-12),
+                },
+                intrinsic_max_nfev=20,
+            )
 
-        self.assertIn(summary["accepted_stage"], {"baseline", "pose"})
+        self.assertEqual(mocked_adjustment.call_count, 2)
+        self.assertEqual(summary["accepted_stage"], "pose")
+        self.assertLess(
+            summary["pose_reprojection_rms"], summary["baseline_reprojection_rms"]
+        )
+        self.assertGreater(
+            summary["intrinsic_reprojection_rms"], summary["pose_reprojection_rms"]
+        )
         self.assertLessEqual(
             summary["final_reprojection_rms"], summary["baseline_reprojection_rms"]
         )
@@ -317,7 +347,7 @@ def test_guarded_two_step_bundle_adjustment_rejects_bad_intrinsics_stage(self):
             summary["baseline_mean_ray_convergence"],
         )
         np.testing.assert_allclose(
-            reprojection_rms(observed_pixels, final_points, final_cals, control),
+            reprojection_rms(observed_pixels, final_points, final_cals, self.control),
             summary["final_reprojection_rms"],
         )
 

From 524b533b48a01a5b6d1d2501592b9b56cc2520e6 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 00:13:07 +0200
Subject: [PATCH 08/27] added synthetic case

---
 .gitignore                                    |    1 +
 docs/bundle_adjustment.md                     |  220 +++-
 openptv_python/demo_bundle_adjustment.py      | 1077 ++++++++++++++++-
 .../generate_synthetic_cavity_case.py         |  518 ++++++++
 openptv_python/orientation.py                 |  193 ++-
 tests/test_bundle_adjustment.py               |  431 +++++++
 tests/test_demo_bundle_adjustment.py          |  240 ++++
 tests/test_synthetic_cavity_case.py           |  270 +++++
 .../test_cavity_synthetic/README.md           |   14 +
 .../test_cavity_synthetic/cal/calblock.txt    |   48 +
 .../test_cavity_synthetic/cal/cam1.tif.addpar |    1 +
 .../test_cavity_synthetic/cal/cam1.tif.ori    |   11 +
 .../test_cavity_synthetic/cal/cam2.tif.addpar |    1 +
 .../test_cavity_synthetic/cal/cam2.tif.ori    |   11 +
 .../test_cavity_synthetic/cal/cam3.tif.addpar |    1 +
 .../test_cavity_synthetic/cal/cam3.tif.ori    |   11 +
 .../test_cavity_synthetic/cal/cam4.tif.addpar |    1 +
 .../test_cavity_synthetic/cal/cam4.tif.ori    |   11 +
 .../calibration_targets/cam1.00001_targets    |   49 +
 .../calibration_targets/cam2.00001_targets    |   49 +
 .../calibration_targets/cam3.00001_targets    |   49 +
 .../calibration_targets/cam4.00001_targets    |   49 +
 .../ground_truth/cal/cam1.tif.addpar          |    1 +
 .../ground_truth/cal/cam1.tif.ori             |   11 +
 .../ground_truth/cal/cam2.tif.addpar          |    1 +
 .../ground_truth/cal/cam2.tif.ori             |   11 +
 .../ground_truth/cal/cam3.tif.addpar          |    1 +
 .../ground_truth/cal/cam3.tif.ori             |   11 +
 .../ground_truth/cal/cam4.tif.addpar          |    1 +
 .../ground_truth/cal/cam4.tif.ori             |   11 +
 .../ground_truth/calibration_body_points.txt  |   48 +
 .../ground_truth/manifest.json                |   18 +
 .../ground_truth/particles/frame_10001.txt    |   97 ++
 .../ground_truth/particles/frame_10002.txt    |   97 ++
 .../img_orig/cam1.10001_targets               |   97 ++
 .../img_orig/cam1.10002_targets               |   97 ++
 .../img_orig/cam2.10001_targets               |   97 ++
 .../img_orig/cam2.10002_targets               |   97 ++
 .../img_orig/cam3.10001_targets               |   97 ++
 .../img_orig/cam3.10002_targets               |   97 ++
 .../img_orig/cam4.10001_targets               |   97 ++
 .../img_orig/cam4.10002_targets               |   97 ++
 .../parameters/cal_ori.par                    |   12 +
 .../parameters/criteria.par                   |   12 +
 .../parameters/detect_plate.par               |   13 +
 .../parameters/dumbbell.par                   |    6 +
 .../parameters/examine.par                    |    2 +
 .../parameters/man_ori.par                    |   16 +
 .../parameters/multi_planes.par               |    4 +
 .../parameters/orient.par                     |   12 +
 .../parameters/pft_version                    |    1 +
 .../parameters/pft_version.par                |    1 +
 .../test_cavity_synthetic/parameters/ptv.par  |   21 +
 .../parameters/sequence.par                   |    6 +
 .../parameters/shaking.par                    |    4 +
 .../parameters/sortgrid.par                   |    1 +
 .../parameters/targ_rec.par                   |   13 +
 .../parameters/track.par                      |    9 +
 .../parameters/unsharp_mask.par               |    1 +
 .../parameters_Run1.yaml                      |  174 +++
 .../res_orig/added.10001                      |   97 ++
 .../res_orig/added.10002                      |   97 ++
 .../res_orig/ptv_is.10001                     |   97 ++
 .../res_orig/ptv_is.10002                     |   97 ++
 .../res_orig/rt_is.10001                      |   97 ++
 .../res_orig/rt_is.10002                      |   97 ++
 66 files changed, 5171 insertions(+), 59 deletions(-)
 create mode 100644 openptv_python/generate_synthetic_cavity_case.py
 create mode 100644 tests/test_demo_bundle_adjustment.py
 create mode 100644 tests/test_synthetic_cavity_case.py
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/README.md
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/cal/calblock.txt
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/cal/cam1.tif.addpar
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/cal/cam1.tif.ori
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/cal/cam2.tif.addpar
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/cal/cam2.tif.ori
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/cal/cam3.tif.addpar
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/cal/cam3.tif.ori
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/cal/cam4.tif.addpar
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/cal/cam4.tif.ori
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam1.00001_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam2.00001_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam3.00001_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam4.00001_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam1.tif.addpar
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam1.tif.ori
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam2.tif.addpar
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam2.tif.ori
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam3.tif.addpar
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam3.tif.ori
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam4.tif.addpar
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam4.tif.ori
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/calibration_body_points.txt
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/manifest.json
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/particles/frame_10001.txt
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/ground_truth/particles/frame_10002.txt
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/img_orig/cam1.10001_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/img_orig/cam1.10002_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/img_orig/cam2.10001_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/img_orig/cam2.10002_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/img_orig/cam3.10001_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/img_orig/cam3.10002_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/img_orig/cam4.10001_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/img_orig/cam4.10002_targets
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/cal_ori.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/criteria.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/detect_plate.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/dumbbell.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/examine.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/man_ori.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/multi_planes.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/orient.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/pft_version
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/pft_version.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/ptv.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/sequence.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/shaking.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/sortgrid.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/targ_rec.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/track.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters/unsharp_mask.par
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/parameters_Run1.yaml
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/res_orig/added.10001
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/res_orig/added.10002
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/res_orig/ptv_is.10001
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/res_orig/ptv_is.10002
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/res_orig/rt_is.10001
 create mode 100644 tests/testing_fodder/test_cavity_synthetic/res_orig/rt_is.10002

diff --git a/.gitignore b/.gitignore
index 39c22cd..9727a52 100644
--- a/.gitignore
+++ b/.gitignore
@@ -471,3 +471,4 @@ $RECYCLE.BIN/
 
 # End of https://www.toptal.com/developers/gitignore/api/python,jupyternotebooks,vim,visualstudiocode,pycharm,emacs,linux,macos,windows
 .cruft.json
+/.tmp
diff --git a/docs/bundle_adjustment.md b/docs/bundle_adjustment.md
index 7c2d2fe..ee39fc0 100644
--- a/docs/bundle_adjustment.md
+++ b/docs/bundle_adjustment.md
@@ -7,7 +7,7 @@ This page documents the bundle-adjustment routines implemented in `openptv_pytho
 The repository currently exposes two related calibration-refinement workflows:
 
 1. `multi_camera_bundle_adjustment`: jointly refine camera parameters and optionally 3D points by minimizing reprojection error.
-2. `guarded_two_step_bundle_adjustment`: run a pose-focused bundle-adjustment stage first, then a tightly constrained intrinsic stage, and reject the second stage if it makes the solution worse.
+1. `guarded_two_step_bundle_adjustment`: run a pose-focused bundle-adjustment stage first, then a tightly constrained intrinsic stage, and reject the second stage if it makes the solution worse.
 
 The demo module compares several configurations on a case folder such as `tests/testing_fodder/test_cavity` and can write one updated calibration folder per experiment.
 
@@ -35,31 +35,33 @@ This is the same layout used by `tests/testing_fodder/test_cavity`.
 
 ## Objective Function
 
-For each observed 2D image point $u_{ij} = (x_{ij}, y_{ij})$ from camera $j$ and 3D point $X_i$, bundle adjustment minimizes reprojection residuals of the form
+For each observed 2D image point $u\_{ij} = (x\_{ij}, y\_{ij})$ from camera $j$ and 3D point $X_i$, bundle adjustment minimizes reprojection residuals of the form
 
 $$
-r_{ij} =
-\begin{bmatrix}
-\hat{x}_{ij}(X_i, \theta_j) - x_{ij} \\
-\hat{y}_{ij}(X_i, \theta_j) - y_{ij}
-\end{bmatrix},
+r\_{ij} =
+\\begin{bmatrix}
+\\hat{x}_{ij}(X_i, \\theta_j) - x_{ij} \\
+\\hat{y}_{ij}(X_i, \\theta_j) - y_{ij}
+\\end{bmatrix},
 $$
 
-where $\theta_j$ denotes the active calibration parameters for camera $j$.
+where $\\theta_j$ denotes the active calibration parameters for camera $j$.
 
 The solver minimizes a robust or linear least-squares objective
 
 $$
 \min_{\Theta, X}
 \sum_{i,j} \rho\left(\left\|W r_{ij}\right\|^2\right)
-+ \sum_k \left(\frac{p_k - p_{k,0}}{\sigma_k}\right)^2,
++ \sum_k \left(\frac{p_k - p_{k,0}}{\sigma_k}\right)^2
++ \sum_{m \in \mathcal{M}} \left\|\frac{X_m - X_m^*}{\sigma^{(X)}_m}\right\|^2.
 $$
 
 with:
 
 1. $W$ converting pixel residuals into normalized units using `pix_x` and `pix_y`.
-2. $\rho(\cdot)$ chosen by the `loss` argument, for example `linear` or `soft_l1`.
-3. Optional Gaussian-style priors on selected parameters via `prior_sigmas`.
+1. $\\rho(\\cdot)$ chosen by the `loss` argument, for example `linear` or `soft_l1`.
+1. Optional Gaussian-style priors on selected parameters via `prior_sigmas`.
+1. Optional soft 3D geometry anchors on selected points via `known_points` and `known_point_sigmas`.
 
 The implementation evaluates the forward projection through OpenPTV's existing camera and multimedia model, so refraction and the Brown-affine distortion model remain part of the optimization.
 
@@ -70,26 +72,31 @@ The implementation evaluates the forward projection through OpenPTV's existing c
 This is the general solver. It can optimize:
 
 1. Exterior parameters: `x0`, `y0`, `z0`, `omega`, `phi`, `kappa`.
-2. Optional intrinsic and distortion parameters controlled by `OrientPar` flags.
-3. The 3D points themselves, if `optimize_points=True`.
+1. Optional intrinsic and distortion parameters controlled by `OrientPar` flags.
+1. The 3D points themselves, if `optimize_points=True`.
 
 Important implementation details:
 
 1. It uses `scipy.optimize.least_squares`.
-2. For `trf` and `dogbox`, the code supplies a Jacobian sparsity pattern so finite differencing scales with the true bundle-adjustment dependency graph instead of a dense matrix.
-3. Residuals are assembled camera-by-camera to reduce Python overhead.
-4. The function guards against scale ambiguity when points and camera poses are both free but too few cameras are fixed.
+1. For `trf` and `dogbox`, the code supplies a Jacobian sparsity pattern so finite differencing scales with the true bundle-adjustment dependency graph instead of a dense matrix.
+1. Residuals are assembled camera-by-camera to reduce Python overhead.
+1. The function guards against scale ambiguity when points and camera poses are both free but too few cameras are fixed.
+1. If `known_points` is provided, the solver appends three residuals per constrained point to keep selected 3D coordinates near supplied target positions.
 
 In practice, the free variables are partitioned as:
 
 $$
-z = [\theta_{j_1}, \theta_{j_2}, \dots, X_1, X_2, \dots].
+z = [\\theta\_{j_1}, \\theta\_{j_2}, \\dots, X_1, X_2, \\dots].
 $$
 
 Each observation residual depends only on:
 
 1. One camera parameter block.
-2. One 3D point block.
+1. One 3D point block.
+
+Each known-point anchor residual depends only on:
+
+1. One 3D point block.
 
 That sparse structure is the reason `jac_sparsity` matters so much for runtime.
 
@@ -98,15 +105,17 @@ That sparse structure is the reason `jac_sparsity` matters so much for runtime.
 This routine is designed for a more conservative refinement flow:
 
 1. Start from baseline calibrations and points.
-2. Run a pose-oriented bundle-adjustment stage.
-3. Run a second intrinsic-focused stage with all cameras fixed in pose.
-4. Accept the intrinsic stage only if it does not degrade reprojection RMS and, by default, does not worsen mean ray convergence.
+1. Run a pose-oriented bundle-adjustment stage.
+1. Run a second intrinsic-focused stage with all cameras fixed in pose.
+1. Accept the intrinsic stage only if it does not degrade reprojection RMS and, by default, does not worsen mean ray convergence.
+
+When `known_points` is supplied, the same soft geometry anchors are applied consistently in both stages.
 
 This gives three possible final outcomes:
 
 1. `baseline`: both optimized stages are rejected.
-2. `pose`: the pose stage is accepted but the intrinsic stage is rejected.
-3. `intrinsics`: both stages are accepted.
+1. `pose`: the pose stage is accepted but the intrinsic stage is rejected.
+1. `intrinsics`: both stages are accepted.
 
 This is useful when the intrinsic update is intentionally tiny and should only be kept if it is clearly beneficial.
 
@@ -115,9 +124,10 @@ This is useful when the intrinsic update is intentionally tiny and should only b
 The current routines report and the demo prints:
 
 1. `initial_reprojection_rms` and `final_reprojection_rms`.
-2. Per-camera reprojection RMS for `multi_camera_bundle_adjustment`.
-3. `baseline_mean_ray_convergence`, `pose_mean_ray_convergence`, and `final_mean_ray_convergence` for guarded runs.
-4. Runtime in seconds for each experiment.
+1. Per-camera reprojection RMS for `multi_camera_bundle_adjustment`.
+1. `baseline_mean_ray_convergence`, `pose_mean_ray_convergence`, and `final_mean_ray_convergence` for guarded runs.
+1. Runtime in seconds for each experiment.
+1. `known_point_indices` for constrained runs in `multi_camera_bundle_adjustment`.
 
 Reprojection RMS answers "how well do the updated cameras explain the observed image measurements?"
 
@@ -140,20 +150,42 @@ By default it uses `tests/testing_fodder/test_cavity`, writes results into `tmp/
 The command-line options are:
 
 1. `case_dir`: optional positional argument pointing at a compatible case folder.
-2. `--max-frames N`: only load the first `N` frames from `sequence.par`.
-3. `--max-points-per-frame N`: only keep the first `N` fully observed points per frame.
-4. `--perturbation-scale S`: scale the deterministic starting pose perturbation.
-5. `--output-dir PATH`: write one output case folder per experiment under this directory.
-6. `--skip-write`: run the experiments without writing updated calibration folders.
+1. `--max-frames N`: only load the first `N` frames from `sequence.par`.
+1. `--max-points-per-frame N`: only keep the first `N` fully observed points per frame.
+1. `--perturbation-scale S`: scale the deterministic starting pose perturbation.
+1. `--output-dir PATH`: write one output case folder per experiment under this directory.
+1. `--skip-write`: run the experiments without writing updated calibration folders.
+1. `--known-points N`: select `N` evenly spaced input 3D points as soft geometry anchors. `0` disables constrained presets.
+1. `--known-point-sigma S`: apply the same object-space sigma to each anchored 3D coordinate.
+1. `--diagnose-fixed-pairs`: run one selected experiment across every two-camera fixed pair instead of running the normal preset table.
+1. `--diagnostic-experiment NAME`: choose which preset to sweep when `--diagnose-fixed-pairs` is enabled.
+1. `--diagnose-epipolar`: compare pairwise epipolar consistency before and after the selected diagnostic experiment.
+1. `--diagnose-quadruplets`: compare leave-one-camera-out quadruplet stability before and after the selected diagnostic experiment.
+1. `--epipolar-curve-points N`: sample `N` points along each epipolar curve when approximating point-to-curve distance in pixels.
+1. `--geometry-guard-mode {auto,off,soft,hard}`: control whether guarded two-step BA rejects stages that move a known 3D calibration target too far from a trusted reference calibration. `auto` enables `hard` when `parameters/cal_ori.par` points to a known 3D target file such as `cal/target_on_a_side.txt`.
+1. `--geometry-guard-threshold S`: pixel threshold used by `hard` geometry guards.
+1. `--geometry-export-threshold S`: refuse to write output case folders whose final calibration-body drift exceeds `S` pixels. Use `0` to disable export blocking.
 
 ### Included Demo Presets
 
-The current demo compares four presets:
+The current demo always compares five unconstrained presets:
 
 1. `pose_trf_linear`: pose-only bundle adjustment with `method="trf"` and `loss="linear"`.
-2. `pose_soft_l1`: pose-only bundle adjustment with robust `soft_l1` loss.
-3. `pose_fixed_points`: pose-only bundle adjustment with `optimize_points=False`.
-4. `guarded_two_step`: pose stage plus tightly constrained intrinsic stage.
+1. `pose_soft_l1`: pose-only bundle adjustment with robust `soft_l1` loss.
+1. `pose_fixed_points`: pose-only bundle adjustment with `optimize_points=False`.
+1. `intrinsics_only`: the demo resets to the reference camera poses, perturbs only `k1`, `p1`, and `p2`, and then refines just those intrinsic terms while all camera poses and 3D points remain fixed.
+1. `guarded_two_step`: pose stage plus tightly constrained intrinsic stage.
+
+When a case exposes known 3D calibration-target points through `cal_ori.par`, the demo now defaults to a geometry-preserving workflow:
+
+1. Guarded presets use a `hard` acceptance check against the known target projections.
+1. Output case folders are written only if the final calibration stays within the configured export drift threshold.
+1. Cases without a known 3D target file still run normally; the geometry guard simply stays off unless you explicitly enable it another way.
+
+If `--known-points` is greater than zero, the demo also compares two constrained presets:
+
+1. `pose_trf_known_points`: pose-only bundle adjustment with soft 3D anchors.
+1. `guarded_two_step_known_points`: guarded two-step refinement with the same 3D anchors active in both stages.
 
 ### Example Commands
 
@@ -173,15 +205,78 @@ python -m openptv_python.demo_bundle_adjustment \
   tests/testing_fodder/test_cavity \
   --max-frames 2 \
   --max-points-per-frame 80 \
+  --known-points 12 \
+  --known-point-sigma 0.25 \
   --output-dir .tmp/demo_bundle_adjustment_runs
 ```
 
+Run the same case but disable automatic geometry blocking if you intentionally want to inspect all candidate exports:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment \
+  tests/testing_fodder/test_cavity \
+  --max-frames 2 \
+  --max-points-per-frame 80 \
+  --known-points 12 \
+  --known-point-sigma 0.25 \
+  --geometry-guard-mode off \
+  --geometry-export-threshold 0 \
+  --output-dir .tmp/demo_bundle_adjustment_runs_all
+```
+
+Keep export blocking but switch guarded acceptance to a softer monotonic geometry check:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment \
+  tests/testing_fodder/test_cavity \
+  --max-frames 2 \
+  --max-points-per-frame 80 \
+  --geometry-guard-mode soft \
+  --geometry-export-threshold 2.5 \
+  --output-dir .tmp/demo_bundle_adjustment_runs_soft
+```
+
 Run without writing calibration folders:
 
 ```bash
 python -m openptv_python.demo_bundle_adjustment --skip-write
 ```
 
+Disable constrained presets entirely:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment --known-points 0 --skip-write
+```
+
+Sweep all two-camera anchor pairs for the guarded solver:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment \
+  tests/testing_fodder/test_cavity \
+  --max-frames 2 \
+  --max-points-per-frame 80 \
+  --known-points 12 \
+  --known-point-sigma 0.25 \
+  --skip-write \
+  --diagnose-fixed-pairs \
+  --diagnostic-experiment guarded_two_step
+```
+
+Compare epipolar consistency and quadruplet sensitivity before and after one guarded run:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment \
+  tests/testing_fodder/test_cavity \
+  --max-frames 2 \
+  --max-points-per-frame 80 \
+  --known-points 12 \
+  --known-point-sigma 0.25 \
+  --skip-write \
+  --diagnostic-experiment guarded_two_step \
+  --diagnose-epipolar \
+  --diagnose-quadruplets
+```
+
 ## Output Folders
 
 When writing is enabled, each experiment produces a case copy like:
@@ -194,10 +289,13 @@ tmp/bundle_adjustment_demo/
       cam1.tif.addpar
       ...
     calibration_delta.txt
+    geometry_check.txt
 ```
 
 `calibration_delta.txt` is generated with `openptv_python.calibration_compare` and shows camera-by-camera parameter differences relative to the source case.
 
+`geometry_check.txt` reports per-camera drift of the known 3D calibration-body projections relative to the trusted reference calibration. If the final drift exceeds `--geometry-export-threshold`, the demo does not write that experiment's case folder at all.
+
 ## Choosing Options
 
 These are the main tradeoffs when selecting settings.
@@ -205,37 +303,73 @@ These are the main tradeoffs when selecting settings.
 ### Loss Function
 
 1. `linear`: use when correspondences are trusted and you want a pure least-squares solution.
-2. `soft_l1`: use when some observations may behave like outliers.
+1. `soft_l1`: use when some observations may behave like outliers.
 
 ### Optimizing Points
 
 1. `optimize_points=True`: stronger joint refinement, but more variables and slower solves.
-2. `optimize_points=False`: faster, more stable if initial 3D points are already good enough.
+1. `optimize_points=False`: faster, more stable if initial 3D points are already good enough.
+
+The `intrinsics_only` preset deliberately uses `optimize_points=False` so the run changes only the selected intrinsic parameters and leaves both poses and 3D points untouched.
+
+Known-point constraints currently require `optimize_points=True` because the anchors act directly on the point coordinates.
+
+### Known 3D Point Anchors
+
+1. Use `known_points` when some reconstructed 3D points correspond to trusted reference-object coordinates or to positions you deliberately want to preserve.
+1. Smaller `known_point_sigmas` keep the selected points closer to their supplied coordinates.
+1. Larger `known_point_sigmas` let reprojection error dominate while still damping large geometric drift.
+1. In the demo, anchored points are selected from the loaded input 3D points, so the constrained presets show how soft geometry anchoring changes the solution relative to the unconstrained runs rather than injecting an external ground-truth object.
 
 ### Fixed Cameras
 
 Fixing multiple cameras helps remove similarity-gauge ambiguity. If both points and poses are free, the implementation requires either:
 
 1. At least two fixed cameras.
-2. Or translation priors for `x0`, `y0`, and `z0`.
+1. Or translation priors for `x0`, `y0`, and `z0`.
+
+If you suspect that one camera is drifting more than the others, the first thing to test is not a single "best" fixed camera. A single fixed camera is still gauge-ambiguous for free-point bundle adjustment. The more defensible diagnostic is to sweep all two-camera fixed pairs and compare:
+
+1. Final reprojection RMS.
+1. Final mean ray convergence.
+1. Whether the fixed cameras stayed numerically unchanged.
+1. How much the free cameras moved relative to the starting calibration.
+
+The demo's `--diagnose-fixed-pairs` mode prints exactly this table so you can see whether the suspicious motion is tied to one anchor pair or appears across many pairs.
+
+### Epipolar And Quadruplet Diagnostics
+
+The new diagnostic mode is intended for the situation where reprojection error improves but calibration-body structure or correspondence quality still looks wrong.
+
+`--diagnose-epipolar` reports pairwise point-to-epipolar-curve distances in pixels for every camera pair. This is not yet an optimization term in bundle adjustment; it is a diagnostic on the observed image correspondences under the current calibration.
+
+`--diagnose-quadruplets` runs a leave-one-camera-out reconstruction check on fully observed points and reports how much the reconstructed 3D point moves when one camera is omitted. Large spreads indicate unstable quadruplets or geometry that is highly sensitive to one view.
+
+This split is useful because the two diagnostics answer different questions:
+
+1. Epipolar distance asks whether the image correspondences are pairwise consistent with the current camera geometry.
+1. Quadruplet sensitivity asks whether a nominal four-camera match remains stable when one view is removed.
+1. Reprojection RMS asks whether the final 3D points and cameras explain the full observation set.
+
+If epipolar distance stays bad while reprojection improves, the next step is usually not to add an epipolar term blindly. First identify whether a subset of camera pairs or quadruplets is driving the inconsistency, then decide whether to reject or downweight those observations.
 
 ### Priors and Bounds
 
 1. `prior_sigmas` softly regularize motion away from the starting calibration.
-2. `parameter_bounds` clip the allowed movement relative to the starting calibration.
+1. `parameter_bounds` clip the allowed movement relative to the starting calibration.
 
 This is especially useful for guarded intrinsic updates where you only want very small corrections.
 
 ## Practical Notes
 
 1. `lm` can be effective on small synthetic problems, but `trf` is generally the better default for larger real cases because it supports bounds and Jacobian sparsity.
-2. Real-case runtime depends strongly on the number of frames and points included.
-3. `read_targets` currently prints filenames as it loads target files, so demo output includes those lines.
+1. Real-case runtime depends strongly on the number of frames and points included.
+1. `read_targets` currently prints filenames as it loads target files, so demo output includes those lines.
 
 ## Related Code
 
 The main implementation lives in:
 
 1. `openptv_python.orientation`
-2. `openptv_python.demo_bundle_adjustment`
-3. `openptv_python.calibration_compare`
+1. `openptv_python.demo_bundle_adjustment`
+1. `openptv_python.calibration_compare`
diff --git a/openptv_python/demo_bundle_adjustment.py b/openptv_python/demo_bundle_adjustment.py
index 4fed2db..ebc95f6 100644
--- a/openptv_python/demo_bundle_adjustment.py
+++ b/openptv_python/demo_bundle_adjustment.py
@@ -7,20 +7,36 @@
 from dataclasses import dataclass
 from pathlib import Path
 from time import perf_counter
-from typing import Dict, Iterable, List, cast
+from typing import Dict, Iterable, List, Sequence, cast
 
 import numpy as np
 
 from .calibration import Calibration, read_calibration, write_calibration
-from .calibration_compare import compare_calibration_folders, format_calibration_comparison
+from .calibration_compare import (
+    compare_calibration_folders,
+    format_calibration_comparison,
+)
+from .epi import epipolar_curve
+from .imgcoord import img_coord
 from .orientation import (
     guarded_two_step_bundle_adjustment,
+    initialize_bundle_adjustment_points,
     mean_ray_convergence,
+    metric_observations_from_pixels,
     multi_camera_bundle_adjustment,
     reprojection_rms,
 )
-from .parameters import ControlPar, OrientPar, SequencePar
+from .parameters import (
+    ControlPar,
+    OrientPar,
+    SequencePar,
+    VolumePar,
+    read_cal_ori_parameters,
+    read_volume_par,
+)
+from .sortgrid import read_calblock as read_sortgrid_calblock
 from .tracking_frame_buf import read_path_frame, read_targets
+from .trafo import metric_to_pixel
 
 
 @dataclass(frozen=True)
@@ -47,6 +63,63 @@ class ExperimentResult:
     final_ray_convergence: float
     notes: str
     cal_dir: Path | None
+    fixed_camera_indices: tuple[int, ...]
+    camera_position_shifts: List[float]
+    camera_angle_shifts: List[float]
+    geometry_projection_drift: List[ProjectionDriftSummary] | None = None
+    refined_cals: List[Calibration] | None = None
+    refined_points: np.ndarray | None = None
+
+
+@dataclass
+class FixedCameraDiagnostic:
+    """Summary of one anchor-pair diagnostic run."""
+
+    fixed_camera_indices: tuple[int, int]
+    final_rms: float
+    final_ray_convergence: float
+    fixed_position_shift: float
+    fixed_angle_shift: float
+    mean_free_position_shift: float
+    max_free_position_shift: float
+    mean_free_angle_shift: float
+    notes: str
+
+
+@dataclass
+class CameraPairEpipolarSummary:
+    """Summary of pairwise epipolar consistency for one camera pair."""
+
+    camera_pair: tuple[int, int]
+    mean_distance: float
+    median_distance: float
+    p95_distance: float
+    max_distance: float
+    acceptance_rates: Dict[float, float]
+
+
+@dataclass
+class QuadrupletSensitivitySummary:
+    """Leave-one-camera-out stability summary for fully observed points."""
+
+    num_points: int
+    mean_spread: float
+    median_spread: float
+    p95_spread: float
+    max_spread: float
+    mean_full_ray_convergence: float
+    mean_leave_one_out_ray_convergence: float
+    worst_point_indices: List[int]
+
+
+@dataclass(frozen=True)
+class ProjectionDriftSummary:
+    """Reference calibration-body projection drift for one camera."""
+
+    camera_index: int
+    mean_distance: float
+    p95_distance: float
+    max_distance: float
 
 
 def clone_calibration(cal: Calibration) -> Calibration:
@@ -76,6 +149,159 @@ def perturb_calibrations(cals: List[Calibration], scale: float) -> List[Calibrat
     return perturbed
 
 
+def perturb_intrinsic_parameters(cals: List[Calibration], scale: float) -> List[Calibration]:
+    """Apply deterministic distortion perturbations while keeping poses fixed."""
+    perturbed = [clone_calibration(cal) for cal in cals]
+    deltas = [
+        (2.5e-5, -8.0e-5, 6.0e-5),
+        (-2.0e-5, 7.0e-5, -5.0e-5),
+        (1.8e-5, 5.0e-5, 4.0e-5),
+        (-1.6e-5, -6.0e-5, 5.0e-5),
+    ]
+    for cal, (k1_delta, p1_delta, p2_delta) in zip(perturbed, deltas):
+        radial = cal.get_radial_distortion().copy()
+        radial[0] += k1_delta * scale
+        cal.set_radial_distortion(radial)
+
+        decentering = cal.get_decentering().copy()
+        decentering[0] += p1_delta * scale
+        decentering[1] += p2_delta * scale
+        cal.set_decentering(decentering)
+
+    return perturbed
+
+
+def build_experiment_start_calibrations(
+    spec: ExperimentSpec,
+    *,
+    start_cals: List[Calibration],
+    reference_cals: List[Calibration],
+) -> List[Calibration]:
+    """Construct the initial calibration set for one experiment."""
+    if bool(spec.ba_kwargs.get("use_reference_cals", False)):
+        base_cals = reference_cals
+    else:
+        base_cals = start_cals
+
+    working_cals = [clone_calibration(cal) for cal in base_cals]
+    if bool(spec.ba_kwargs.get("perturb_intrinsics_only", False)):
+        scale = cast(float, spec.ba_kwargs.get("intrinsic_perturbation_scale", 1.0))
+        working_cals = perturb_intrinsic_parameters(working_cals, scale)
+
+    fixed_camera_indices = tuple(
+        cast(List[int] | None, spec.ba_kwargs.get("fixed_camera_indices")) or []
+    )
+    for camera_index in fixed_camera_indices:
+        if camera_index < 0 or camera_index >= len(working_cals):
+            continue
+        working_cals[camera_index].set_pos(reference_cals[camera_index].get_pos().copy())
+        working_cals[camera_index].set_angles(
+            reference_cals[camera_index].get_angles().copy()
+        )
+
+    return working_cals
+
+
+def load_reference_geometry_points(
+    case_dir: Path,
+    num_cams: int,
+) -> np.ndarray | None:
+    """Load known calibration-body points referenced by cal_ori.par, if present."""
+    cal_ori_path = case_dir / "parameters/cal_ori.par"
+    if not cal_ori_path.exists():
+        return None
+
+    calibration_par = read_cal_ori_parameters(cal_ori_path, num_cams)
+    if not calibration_par.fixp_name:
+        return None
+
+    calblock_path = case_dir / calibration_par.fixp_name
+    if not calblock_path.exists():
+        return None
+
+    calblock = read_sortgrid_calblock(calblock_path)
+    if len(calblock) == 0:
+        return None
+
+    return np.column_stack([calblock.x, calblock.y, calblock.z]).astype(float)
+
+
+def calibration_body_projection_drift(
+    reference_cals: Sequence[Calibration],
+    candidate_cals: Sequence[Calibration],
+    control: ControlPar,
+    reference_points: np.ndarray | None,
+) -> List[ProjectionDriftSummary] | None:
+    """Compare candidate calibration-body projections to a trusted reference set."""
+    if reference_points is None:
+        return None
+
+    summaries: List[ProjectionDriftSummary] = []
+    for camera_index, (reference_cal, candidate_cal) in enumerate(
+        zip(reference_cals, candidate_cals),
+        start=1,
+    ):
+        reference_pixels = []
+        candidate_pixels = []
+        for point in reference_points:
+            ref_x, ref_y = img_coord(point, reference_cal, control.mm)
+            cand_x, cand_y = img_coord(point, candidate_cal, control.mm)
+            reference_pixels.append(metric_to_pixel(ref_x, ref_y, control))
+            candidate_pixels.append(metric_to_pixel(cand_x, cand_y, control))
+
+        displacement = np.linalg.norm(
+            np.asarray(candidate_pixels) - np.asarray(reference_pixels),
+            axis=1,
+        )
+        summaries.append(
+            ProjectionDriftSummary(
+                camera_index=camera_index,
+                mean_distance=float(displacement.mean()),
+                p95_distance=float(np.percentile(displacement, 95.0)),
+                max_distance=float(displacement.max()),
+            )
+        )
+
+    return summaries
+
+
+def format_projection_drift(summaries: Sequence[ProjectionDriftSummary] | None) -> str:
+    """Render calibration-body projection drift summaries."""
+    if not summaries:
+        return "No reference calibration-body geometry available."
+
+    lines = ["camera  mean_px  p95_px  max_px", "------  -------  ------  ------"]
+    for item in summaries:
+        lines.append(
+            f"{item.camera_index:>6}  {item.mean_distance:>7.3f}  {item.p95_distance:>6.3f}  {item.max_distance:>6.3f}"
+        )
+    return "\n".join(lines)
+
+
+def max_projection_drift(
+    summaries: Sequence[ProjectionDriftSummary] | None,
+) -> float | None:
+    """Return the worst per-camera maximum projection drift in pixels."""
+    if not summaries:
+        return None
+    return max(item.max_distance for item in summaries)
+
+
+def should_block_export_on_geometry(
+    summaries: Sequence[ProjectionDriftSummary] | None,
+    threshold_px: float | None,
+) -> tuple[bool, str]:
+    """Return whether an export should be blocked by geometry drift."""
+    if summaries is None or threshold_px is None or threshold_px <= 0:
+        return False, ""
+
+    drift_max = max_projection_drift(summaries)
+    if drift_max is None or drift_max <= threshold_px:
+        return False, ""
+
+    return True, f"geometry_export_blocked=max_drift={drift_max:.3f}px>{threshold_px:.3f}px"
+
+
 def discover_num_cams(cal_dir: Path) -> int:
     """Infer the number of cameras from calibration .ori files."""
     return len(sorted(cal_dir.glob("*.ori")))
@@ -151,7 +377,357 @@ def load_case_observations(
     )
 
 
-def default_experiments() -> List[ExperimentSpec]:
+def build_known_point_constraints(
+    point_init: np.ndarray,
+    count: int,
+) -> Dict[int, np.ndarray]:
+    """Select a deterministic subset of 3D points to use as soft anchors."""
+    if count <= 0:
+        return {}
+
+    num_points = int(point_init.shape[0])
+    if num_points == 0:
+        raise ValueError("Cannot build known-point constraints from an empty point set")
+
+    target_count = min(count, num_points)
+    selected_indices = np.linspace(0, num_points - 1, num=target_count, dtype=int)
+    return {
+        int(point_index): point_init[int(point_index)].copy()
+        for point_index in selected_indices.tolist()
+    }
+
+
+def all_fixed_camera_pairs(num_cams: int) -> List[tuple[int, int]]:
+    """Return all unique two-camera anchor pairs."""
+    return [
+        (first, second)
+        for first in range(num_cams - 1)
+        for second in range(first + 1, num_cams)
+    ]
+
+
+def calibration_pose_shifts(
+    reference_cals: Sequence[Calibration],
+    candidate_cals: Sequence[Calibration],
+) -> tuple[List[float], List[float]]:
+    """Measure per-camera pose changes relative to a reference calibration set."""
+    position_shifts = [
+        float(np.linalg.norm(candidate.get_pos() - reference.get_pos()))
+        for reference, candidate in zip(reference_cals, candidate_cals)
+    ]
+    angle_shifts = [
+        float(np.linalg.norm(candidate.get_angles() - reference.get_angles()))
+        for reference, candidate in zip(reference_cals, candidate_cals)
+    ]
+    return position_shifts, angle_shifts
+
+
+def _point_to_polyline_distance(point: np.ndarray, polyline: np.ndarray) -> float:
+    """Return the minimum Euclidean distance from a point to a polyline in pixels."""
+    if polyline.shape[0] < 2:
+        raise ValueError("Polyline must have at least two vertices")
+
+    best = np.inf
+    for start, end in zip(polyline[:-1], polyline[1:]):
+        segment = end - start
+        segment_length_sq = float(np.dot(segment, segment))
+        if segment_length_sq == 0.0:
+            distance = float(np.linalg.norm(point - start))
+        else:
+            t = float(np.dot(point - start, segment) / segment_length_sq)
+            t = min(1.0, max(0.0, t))
+            projection = start + t * segment
+            distance = float(np.linalg.norm(point - projection))
+        best = min(best, distance)
+    return float(best)
+
+
+def symmetric_epipolar_distance(
+    origin_obs: np.ndarray,
+    target_obs: np.ndarray,
+    origin_cal: Calibration,
+    target_cal: Calibration,
+    cpar: ControlPar,
+    vpar: VolumePar,
+    num_curve_points: int,
+) -> float:
+    """Return a symmetric epipolar inconsistency score in pixels."""
+    forward_curve = epipolar_curve(
+        origin_obs,
+        origin_cal,
+        target_cal,
+        num_curve_points,
+        cpar,
+        vpar,
+    )
+    backward_curve = epipolar_curve(
+        target_obs,
+        target_cal,
+        origin_cal,
+        num_curve_points,
+        cpar,
+        vpar,
+    )
+    forward_distance = _point_to_polyline_distance(target_obs, forward_curve)
+    backward_distance = _point_to_polyline_distance(origin_obs, backward_curve)
+    return 0.5 * (forward_distance + backward_distance)
+
+
+def summarize_epipolar_consistency(
+    observed_pixels: np.ndarray,
+    cals: Sequence[Calibration],
+    cpar: ControlPar,
+    vpar: VolumePar,
+    *,
+    num_curve_points: int = 64,
+    threshold_scales: Sequence[float] = (0.5, 1.0, 2.0),
+) -> List[CameraPairEpipolarSummary]:
+    """Summarize pairwise epipolar distance statistics for observed correspondences."""
+    thresholds = [float(vpar.eps0 * scale) for scale in threshold_scales]
+    summaries = []
+    for cam_a, cam_b in all_fixed_camera_pairs(len(cals)):
+        distances = []
+        for point_index in range(observed_pixels.shape[0]):
+            obs_a = observed_pixels[point_index, cam_a]
+            obs_b = observed_pixels[point_index, cam_b]
+            if not (np.all(np.isfinite(obs_a)) and np.all(np.isfinite(obs_b))):
+                continue
+            distances.append(
+                symmetric_epipolar_distance(
+                    obs_a,
+                    obs_b,
+                    cals[cam_a],
+                    cals[cam_b],
+                    cpar,
+                    vpar,
+                    num_curve_points,
+                )
+            )
+
+        if not distances:
+            continue
+        distance_array = np.asarray(distances, dtype=np.float64)
+        summaries.append(
+            CameraPairEpipolarSummary(
+                camera_pair=(cam_a, cam_b),
+                mean_distance=float(np.mean(distance_array)),
+                median_distance=float(np.median(distance_array)),
+                p95_distance=float(np.percentile(distance_array, 95.0)),
+                max_distance=float(np.max(distance_array)),
+                acceptance_rates={
+                    threshold: float(np.mean(distance_array <= threshold))
+                    for threshold in thresholds
+                },
+            )
+        )
+
+    summaries.sort(key=lambda item: item.mean_distance)
+    return summaries
+
+
+def format_epipolar_diagnostics(
+    summaries: Sequence[CameraPairEpipolarSummary],
+) -> str:
+    """Render pairwise epipolar statistics as a compact table."""
+    thresholds = sorted(
+        {threshold for item in summaries for threshold in item.acceptance_rates}
+    )
+    headers = [
+        "pair",
+        "mean_epi",
+        "median_epi",
+        "p95_epi",
+        "max_epi",
+    ]
+    headers.extend([f"<= {threshold:.3f}" for threshold in thresholds])
+    data = []
+    for item in summaries:
+        row = [
+            f"{item.camera_pair[0] + 1},{item.camera_pair[1] + 1}",
+            f"{item.mean_distance:.6f}",
+            f"{item.median_distance:.6f}",
+            f"{item.p95_distance:.6f}",
+            f"{item.max_distance:.6f}",
+        ]
+        row.extend([f"{100.0 * item.acceptance_rates[threshold]:.1f}%" for threshold in thresholds])
+        data.append(row)
+
+    widths = [len(header) for header in headers]
+    for row in data:
+        for index, value in enumerate(row):
+            widths[index] = max(widths[index], len(value))
+
+    def render_row(values: List[str]) -> str:
+        return "  ".join(value.ljust(widths[index]) for index, value in enumerate(values))
+
+    separator = "  ".join("-" * width for width in widths)
+    lines = [render_row(headers), separator]
+    lines.extend(render_row(row) for row in data)
+    return "\n".join(lines)
+
+
+def summarize_quadruplet_sensitivity(
+    observed_pixels: np.ndarray,
+    cals: Sequence[Calibration],
+    cpar: ControlPar,
+) -> QuadrupletSensitivitySummary:
+    """Measure leave-one-camera-out instability for fully observed points."""
+    if observed_pixels.shape[1] < 4:
+        raise ValueError("Quadruplet sensitivity requires at least four cameras")
+
+    full_mask = np.all(np.isfinite(observed_pixels), axis=(1, 2))
+    full_indices = np.flatnonzero(full_mask)
+    if full_indices.size == 0:
+        raise ValueError("No fully observed quadruplets available for sensitivity analysis")
+
+    full_points, full_ray_convergence = initialize_bundle_adjustment_points(
+        observed_pixels[full_indices],
+        list(cals),
+        cpar,
+    )
+
+    spreads = np.empty(full_indices.size, dtype=np.float64)
+    leave_one_out_rays = np.empty(full_indices.size, dtype=np.float64)
+    for local_index, point_index in enumerate(full_indices):
+        subset_points = []
+        subset_rays = []
+        for omitted_camera in range(observed_pixels.shape[1]):
+            keep = [cam for cam in range(observed_pixels.shape[1]) if cam != omitted_camera]
+            subset_observed = observed_pixels[point_index : point_index + 1, keep, :]
+            subset_cals = [cals[cam] for cam in keep]
+            subset_point, subset_ray = initialize_bundle_adjustment_points(
+                subset_observed,
+                subset_cals,
+                cpar,
+            )
+            subset_points.append(subset_point[0])
+            subset_rays.append(float(subset_ray[0]))
+
+        subset_stack = np.asarray(subset_points, dtype=np.float64)
+        spreads[local_index] = float(
+            np.max(np.linalg.norm(subset_stack - full_points[local_index], axis=1))
+        )
+        leave_one_out_rays[local_index] = float(np.mean(subset_rays))
+
+    worst_order = np.argsort(spreads)[::-1][:5]
+    return QuadrupletSensitivitySummary(
+        num_points=int(full_indices.size),
+        mean_spread=float(np.mean(spreads)),
+        median_spread=float(np.median(spreads)),
+        p95_spread=float(np.percentile(spreads, 95.0)),
+        max_spread=float(np.max(spreads)),
+        mean_full_ray_convergence=float(np.mean(full_ray_convergence)),
+        mean_leave_one_out_ray_convergence=float(np.mean(leave_one_out_rays)),
+        worst_point_indices=[int(full_indices[index]) for index in worst_order.tolist()],
+    )
+
+
+def format_quadruplet_sensitivity(
+    baseline: QuadrupletSensitivitySummary,
+    final: QuadrupletSensitivitySummary,
+) -> str:
+    """Render before/after quadruplet sensitivity diagnostics."""
+    headers = (
+        "phase",
+        "points",
+        "mean_spread",
+        "median_spread",
+        "p95_spread",
+        "max_spread",
+        "mean_full_ray",
+        "mean_loo_ray",
+        "worst_points",
+    )
+    data = []
+    for phase, summary in (("baseline", baseline), ("final", final)):
+        data.append(
+            [
+                phase,
+                str(summary.num_points),
+                f"{summary.mean_spread:.6f}",
+                f"{summary.median_spread:.6f}",
+                f"{summary.p95_spread:.6f}",
+                f"{summary.max_spread:.6f}",
+                f"{summary.mean_full_ray_convergence:.6f}",
+                f"{summary.mean_leave_one_out_ray_convergence:.6f}",
+                ",".join(str(index) for index in summary.worst_point_indices),
+            ]
+        )
+
+    widths = [len(header) for header in headers]
+    for row in data:
+        for index, value in enumerate(row):
+            widths[index] = max(widths[index], len(value))
+
+    def render_row(values: List[str]) -> str:
+        return "  ".join(value.ljust(widths[index]) for index, value in enumerate(values))
+
+    separator = "  ".join("-" * width for width in widths)
+    lines = [render_row(list(headers)), separator]
+    lines.extend(render_row(row) for row in data)
+    return "\n".join(lines)
+
+
+def summarize_fixed_camera_diagnostics(
+    results: Sequence[ExperimentResult],
+) -> List[FixedCameraDiagnostic]:
+    """Collapse experiment results into anchor-pair diagnostic metrics."""
+    diagnostics = []
+    for result in results:
+        if len(result.fixed_camera_indices) != 2:
+            raise ValueError("Fixed-camera diagnostics require exactly two fixed cameras")
+
+        fixed_indices = set(result.fixed_camera_indices)
+        fixed_position_shifts = [
+            result.camera_position_shifts[index] for index in result.fixed_camera_indices
+        ]
+        fixed_angle_shifts = [
+            result.camera_angle_shifts[index] for index in result.fixed_camera_indices
+        ]
+        free_position_shifts = [
+            shift
+            for index, shift in enumerate(result.camera_position_shifts)
+            if index not in fixed_indices
+        ]
+        free_angle_shifts = [
+            shift
+            for index, shift in enumerate(result.camera_angle_shifts)
+            if index not in fixed_indices
+        ]
+
+        diagnostics.append(
+            FixedCameraDiagnostic(
+                fixed_camera_indices=cast(tuple[int, int], result.fixed_camera_indices),
+                final_rms=result.final_rms,
+                final_ray_convergence=result.final_ray_convergence,
+                fixed_position_shift=max(fixed_position_shifts, default=0.0),
+                fixed_angle_shift=max(fixed_angle_shifts, default=0.0),
+                mean_free_position_shift=float(np.mean(free_position_shifts)),
+                max_free_position_shift=max(free_position_shifts, default=0.0),
+                mean_free_angle_shift=float(np.mean(free_angle_shifts)),
+                notes=result.notes,
+            )
+        )
+
+    diagnostics.sort(
+        key=lambda item: (
+            item.fixed_position_shift,
+            item.fixed_angle_shift,
+            item.final_rms,
+            item.final_ray_convergence,
+        )
+    )
+    return diagnostics
+
+
+def default_experiments(
+    *,
+    known_points: Dict[int, np.ndarray] | None = None,
+    known_point_sigmas: float | np.ndarray | None = None,
+    perturbation_scale: float = 1.0,
+    geometry_guard_mode: str = "off",
+    geometry_guard_threshold: float | None = None,
+) -> List[ExperimentSpec]:
     """Return a set of representative BA configurations."""
     pose_priors = {
         "x0": 0.5,
@@ -199,7 +775,12 @@ def default_experiments() -> List[ExperimentSpec]:
     intrinsics.p1flag = 1
     intrinsics.p2flag = 1
 
-    return [
+    intrinsics_only = OrientPar()
+    intrinsics_only.k1flag = 1
+    intrinsics_only.p1flag = 1
+    intrinsics_only.p2flag = 1
+
+    experiments = [
         ExperimentSpec(
             name="pose_trf_linear",
             description="Pose-only BA with linear loss and TRF",
@@ -244,6 +825,33 @@ def default_experiments() -> List[ExperimentSpec]:
                 "optimize_points": False,
             },
         ),
+        ExperimentSpec(
+            name="intrinsics_only",
+            description="Intrinsics-only BA from fixed reference poses with tightly bounded distortion updates",
+            mode="multi",
+            ba_kwargs={
+                "orient_par": intrinsics_only,
+                "fixed_camera_indices": [0, 1, 2, 3],
+                "loss": "linear",
+                "method": "trf",
+                "prior_sigmas": {
+                    "k1": 5e-5,
+                    "p1": 1e-4,
+                    "p2": 1e-4,
+                },
+                "parameter_bounds": {
+                    "k1": (-5e-5, 5e-5),
+                    "p1": (-2e-4, 2e-4),
+                    "p2": (-2e-4, 2e-4),
+                },
+                "max_nfev": 40,
+                "optimize_extrinsics": False,
+                "optimize_points": False,
+                "use_reference_cals": True,
+                "perturb_intrinsics_only": True,
+                "intrinsic_perturbation_scale": perturbation_scale,
+            },
+        ),
         ExperimentSpec(
             name="guarded_two_step",
             description="Pose stage followed by tightly constrained intrinsic stage",
@@ -258,10 +866,56 @@ def default_experiments() -> List[ExperimentSpec]:
                 "intrinsic_prior_sigmas": tight_intrinsic_priors,
                 "intrinsic_parameter_bounds": tight_intrinsic_bounds,
                 "intrinsic_max_nfev": 4,
+                "geometry_guard_mode": geometry_guard_mode,
+                "geometry_guard_threshold": geometry_guard_threshold,
             },
         ),
     ]
 
+    if known_points:
+        experiments.extend(
+            [
+                ExperimentSpec(
+                    name="pose_trf_known_points",
+                    description="Pose-only BA with soft known-point geometry anchors",
+                    mode="multi",
+                    ba_kwargs={
+                        "orient_par": OrientPar(),
+                        "fixed_camera_indices": [0, 1],
+                        "loss": "linear",
+                        "method": "trf",
+                        "prior_sigmas": pose_priors,
+                        "parameter_bounds": pose_bounds,
+                        "max_nfev": 12,
+                        "known_points": known_points,
+                        "known_point_sigmas": known_point_sigmas,
+                    },
+                ),
+                ExperimentSpec(
+                    name="guarded_two_step_known_points",
+                    description="Guarded two-step BA with soft known-point geometry anchors",
+                    mode="guarded",
+                    ba_kwargs={
+                        "pose_orient_par": OrientPar(),
+                        "intrinsic_orient_par": intrinsics,
+                        "fixed_camera_indices": [0, 1],
+                        "pose_prior_sigmas": pose_priors,
+                        "pose_parameter_bounds": pose_bounds,
+                        "pose_max_nfev": 8,
+                        "intrinsic_prior_sigmas": tight_intrinsic_priors,
+                        "intrinsic_parameter_bounds": tight_intrinsic_bounds,
+                        "intrinsic_max_nfev": 4,
+                        "known_points": known_points,
+                        "known_point_sigmas": known_point_sigmas,
+                        "geometry_guard_mode": geometry_guard_mode,
+                        "geometry_guard_threshold": geometry_guard_threshold,
+                    },
+                ),
+            ]
+        )
+
+    return experiments
+
 
 def ensure_output_case_layout(source_case_dir: Path, output_case_dir: Path) -> Path:
     """Copy the source case and return the writable calibration directory."""
@@ -286,11 +940,21 @@ def run_experiment(
     point_init: np.ndarray,
     control: ControlPar,
     start_cals: List[Calibration],
+    reference_cals: List[Calibration],
+    reference_geometry_points: np.ndarray | None,
+    geometry_export_threshold: float | None,
     source_case_dir: Path,
     output_dir: Path | None,
 ) -> ExperimentResult:
     """Execute one experiment and collect metrics and optional outputs."""
-    working_cals = [clone_calibration(cal) for cal in start_cals]
+    working_cals = build_experiment_start_calibrations(
+        spec,
+        start_cals=start_cals,
+        reference_cals=reference_cals,
+    )
+    fixed_camera_indices = tuple(
+        cast(List[int] | None, spec.ba_kwargs.get("fixed_camera_indices")) or []
+    )
     baseline_rms = reprojection_rms(observed_pixels, point_init, working_cals, control)
     baseline_ray = mean_ray_convergence(observed_pixels, working_cals, control)
 
@@ -326,6 +990,14 @@ def run_experiment(
                 spec.ba_kwargs.get("optimize_extrinsics", True),
             ),
             optimize_points=cast(bool, spec.ba_kwargs.get("optimize_points", True)),
+            known_points=cast(
+                Dict[int, np.ndarray] | None,
+                spec.ba_kwargs.get("known_points"),
+            ),
+            known_point_sigmas=cast(
+                float | np.ndarray | None,
+                spec.ba_kwargs.get("known_point_sigmas"),
+            ),
             ftol=cast(float | None, spec.ba_kwargs.get("ftol")),
             xtol=cast(float | None, spec.ba_kwargs.get("xtol")),
             gtol=cast(float | None, spec.ba_kwargs.get("gtol")),
@@ -379,9 +1051,15 @@ def run_experiment(
                 int | None,
                 spec.ba_kwargs.get("intrinsic_max_nfev"),
             ),
-            intrinsic_ftol=cast(float | None, spec.ba_kwargs.get("intrinsic_ftol", 1e-12)),
-            intrinsic_xtol=cast(float | None, spec.ba_kwargs.get("intrinsic_xtol", 1e-12)),
-            intrinsic_gtol=cast(float | None, spec.ba_kwargs.get("intrinsic_gtol", 1e-12)),
+            intrinsic_ftol=cast(
+                float | None, spec.ba_kwargs.get("intrinsic_ftol", 1e-12)
+            ),
+            intrinsic_xtol=cast(
+                float | None, spec.ba_kwargs.get("intrinsic_xtol", 1e-12)
+            ),
+            intrinsic_gtol=cast(
+                float | None, spec.ba_kwargs.get("intrinsic_gtol", 1e-12)
+            ),
             pose_optimize_points=cast(
                 bool,
                 spec.ba_kwargs.get("pose_optimize_points", True),
@@ -398,6 +1076,24 @@ def run_experiment(
                 bool,
                 spec.ba_kwargs.get("reject_on_ray_convergence", True),
             ),
+            known_points=cast(
+                Dict[int, np.ndarray] | None,
+                spec.ba_kwargs.get("known_points"),
+            ),
+            known_point_sigmas=cast(
+                float | np.ndarray | None,
+                spec.ba_kwargs.get("known_point_sigmas"),
+            ),
+            geometry_reference_points=reference_geometry_points,
+            geometry_reference_cals=reference_cals,
+            geometry_guard_mode=cast(
+                str,
+                spec.ba_kwargs.get("geometry_guard_mode", "off"),
+            ),
+            geometry_guard_threshold=cast(
+                float | None,
+                spec.ba_kwargs.get("geometry_guard_threshold"),
+            ),
         )
         success = True
         final_rms = cast(float, summary["final_reprojection_rms"])
@@ -407,9 +1103,27 @@ def run_experiment(
         raise ValueError(f"Unknown experiment mode: {spec.mode}")
 
     duration_sec = perf_counter() - start
+    camera_position_shifts, camera_angle_shifts = calibration_pose_shifts(
+        working_cals,
+        refined_cals,
+    )
+    geometry_projection_drift = calibration_body_projection_drift(
+        reference_cals,
+        refined_cals,
+        control,
+        reference_geometry_points,
+    )
+    export_blocked, export_note = should_block_export_on_geometry(
+        geometry_projection_drift,
+        geometry_export_threshold,
+    )
+    if export_note:
+        notes = f"{notes}; {export_note}" if notes else export_note
+    if export_blocked:
+        success = False
 
     cal_dir = None
-    if output_dir is not None:
+    if output_dir is not None and not export_blocked:
         case_out_dir = output_dir / spec.name
         cal_dir = ensure_output_case_layout(source_case_dir, case_out_dir)
         write_calibration_folder(refined_cals, cal_dir)
@@ -423,6 +1137,10 @@ def run_experiment(
             + "\n",
             encoding="utf-8",
         )
+        (case_out_dir / "geometry_check.txt").write_text(
+            format_projection_drift(geometry_projection_drift) + "\n",
+            encoding="utf-8",
+        )
 
     return ExperimentResult(
         name=spec.name,
@@ -435,6 +1153,12 @@ def run_experiment(
         final_ray_convergence=final_ray,
         notes=notes,
         cal_dir=cal_dir,
+        fixed_camera_indices=fixed_camera_indices,
+        camera_position_shifts=camera_position_shifts,
+        camera_angle_shifts=camera_angle_shifts,
+        geometry_projection_drift=geometry_projection_drift,
+        refined_cals=refined_cals,
+        refined_points=refined_points,
     )
 
 
@@ -469,6 +1193,51 @@ def format_results(results: Iterable[ExperimentResult]) -> str:
         for index, value in enumerate(row):
             widths[index] = max(widths[index], len(value))
 
+    def render_row(values: List[str]) -> str:
+        return "  ".join(
+            value.ljust(widths[index]) for index, value in enumerate(values)
+        )
+
+    separator = "  ".join("-" * width for width in widths)
+    lines = [render_row(list(headers)), separator]
+    lines.extend(render_row(row) for row in data)
+    return "\n".join(lines)
+
+
+def format_fixed_camera_diagnostics(
+    diagnostics: Sequence[FixedCameraDiagnostic],
+) -> str:
+    """Render a compact plain-text summary of anchor-pair diagnostics."""
+    headers = (
+        "fixed_pair",
+        "rms_after",
+        "ray_after",
+        "fixed_pos_shift",
+        "fixed_ang_shift",
+        "mean_free_pos",
+        "max_free_pos",
+        "mean_free_ang",
+        "notes",
+    )
+    data = [
+        [
+            f"{item.fixed_camera_indices[0] + 1},{item.fixed_camera_indices[1] + 1}",
+            f"{item.final_rms:.6f}",
+            f"{item.final_ray_convergence:.6f}",
+            f"{item.fixed_position_shift:.6e}",
+            f"{item.fixed_angle_shift:.6e}",
+            f"{item.mean_free_position_shift:.6f}",
+            f"{item.max_free_position_shift:.6f}",
+            f"{item.mean_free_angle_shift:.6f}",
+            item.notes,
+        ]
+        for item in diagnostics
+    ]
+    widths = [len(header) for header in headers]
+    for row in data:
+        for index, value in enumerate(row):
+            widths[index] = max(widths[index], len(value))
+
     def render_row(values: List[str]) -> str:
         return "  ".join(value.ljust(widths[index]) for index, value in enumerate(values))
 
@@ -478,6 +1247,103 @@ def render_row(values: List[str]) -> str:
     return "\n".join(lines)
 
 
+def summarize_anchor_participation(
+    diagnostics: Sequence[FixedCameraDiagnostic],
+    num_cams: int,
+) -> str:
+    """Aggregate anchor-pair diagnostics by camera participation."""
+    headers = (
+        "camera",
+        "pair_count",
+        "avg_rms",
+        "avg_ray",
+        "avg_free_pos",
+        "avg_free_ang",
+    )
+    rows = []
+    for camera_index in range(num_cams):
+        participating = [
+            item for item in diagnostics if camera_index in item.fixed_camera_indices
+        ]
+        if not participating:
+            continue
+        rows.append(
+            [
+                str(camera_index + 1),
+                str(len(participating)),
+                f"{np.mean([item.final_rms for item in participating]):.6f}",
+                f"{np.mean([item.final_ray_convergence for item in participating]):.6f}",
+                f"{np.mean([item.mean_free_position_shift for item in participating]):.6f}",
+                f"{np.mean([item.mean_free_angle_shift for item in participating]):.6f}",
+            ]
+        )
+
+    widths = [len(header) for header in headers]
+    for row in rows:
+        for index, value in enumerate(row):
+            widths[index] = max(widths[index], len(value))
+
+    def render_row(values: List[str]) -> str:
+        return "  ".join(value.ljust(widths[index]) for index, value in enumerate(values))
+
+    separator = "  ".join("-" * width for width in widths)
+    lines = [render_row(list(headers)), separator]
+    lines.extend(render_row(row) for row in rows)
+    return "\n".join(lines)
+
+
+def find_experiment_spec(
+    experiments: Sequence[ExperimentSpec],
+    name: str,
+) -> ExperimentSpec:
+    """Return one experiment by name."""
+    for spec in experiments:
+        if spec.name == name:
+            return spec
+    raise ValueError(f"Unknown experiment: {name}")
+
+
+def run_fixed_camera_pair_diagnostics(
+    spec: ExperimentSpec,
+    *,
+    observed_pixels: np.ndarray,
+    point_init: np.ndarray,
+    control: ControlPar,
+    start_cals: List[Calibration],
+    reference_cals: List[Calibration],
+    reference_geometry_points: np.ndarray | None,
+    geometry_export_threshold: float | None,
+    source_case_dir: Path,
+) -> List[FixedCameraDiagnostic]:
+    """Run one experiment over every two-camera anchor pair and summarize results."""
+    diagnostic_results = []
+    for fixed_pair in all_fixed_camera_pairs(len(start_cals)):
+        pair_kwargs = dict(spec.ba_kwargs)
+        pair_kwargs["fixed_camera_indices"] = list(fixed_pair)
+        pair_spec = ExperimentSpec(
+            name=f"{spec.name}_fixed_{fixed_pair[0] + 1}_{fixed_pair[1] + 1}",
+            description=f"{spec.description} with cameras {fixed_pair[0] + 1} and {fixed_pair[1] + 1} fixed",
+            mode=spec.mode,
+            ba_kwargs=pair_kwargs,
+        )
+        diagnostic_results.append(
+            run_experiment(
+                pair_spec,
+                observed_pixels=observed_pixels,
+                point_init=point_init,
+                control=control,
+                start_cals=start_cals,
+                reference_cals=reference_cals,
+                reference_geometry_points=reference_geometry_points,
+                geometry_export_threshold=geometry_export_threshold,
+                source_case_dir=source_case_dir,
+                output_dir=None,
+            )
+        )
+
+    return summarize_fixed_camera_diagnostics(diagnostic_results)
+
+
 def parse_args(argv: Iterable[str] | None = None) -> argparse.Namespace:
     """Parse command-line arguments for the BA demo."""
     parser = argparse.ArgumentParser(
@@ -519,6 +1385,64 @@ def parse_args(argv: Iterable[str] | None = None) -> argparse.Namespace:
         action="store_true",
         help="Run comparisons but do not write updated calibration folders.",
     )
+    parser.add_argument(
+        "--known-points",
+        type=int,
+        default=12,
+        help="Use N evenly spaced input 3D points as soft geometry anchors; 0 disables constrained presets.",
+    )
+    parser.add_argument(
+        "--known-point-sigma",
+        type=float,
+        default=0.25,
+        help="Standard deviation for each anchored 3D coordinate in object-space units.",
+    )
+    parser.add_argument(
+        "--diagnose-fixed-pairs",
+        action="store_true",
+        help="Run one experiment across every two-camera fixed pair and print anchor-pair diagnostics.",
+    )
+    parser.add_argument(
+        "--diagnostic-experiment",
+        type=str,
+        default="guarded_two_step",
+        help="Experiment name to use with --diagnose-fixed-pairs.",
+    )
+    parser.add_argument(
+        "--diagnose-epipolar",
+        action="store_true",
+        help="Compare pairwise epipolar consistency before and after the selected diagnostic experiment.",
+    )
+    parser.add_argument(
+        "--diagnose-quadruplets",
+        action="store_true",
+        help="Compare leave-one-camera-out quadruplet stability before and after the selected diagnostic experiment.",
+    )
+    parser.add_argument(
+        "--epipolar-curve-points",
+        type=int,
+        default=64,
+        help="Number of points sampled along each epipolar curve for the diagnostic distance calculation.",
+    )
+    parser.add_argument(
+        "--geometry-guard-mode",
+        type=str,
+        choices=("auto", "off", "soft", "hard"),
+        default="auto",
+        help="Guarded-BA geometry acceptance mode. 'auto' uses 'hard' when cal_ori.par exposes known 3D target points.",
+    )
+    parser.add_argument(
+        "--geometry-guard-threshold",
+        type=float,
+        default=2.5,
+        help="Maximum allowed calibration-body projection drift in pixels for hard geometry guards.",
+    )
+    parser.add_argument(
+        "--geometry-export-threshold",
+        type=float,
+        default=2.5,
+        help="Do not write output case folders whose final calibration-body drift exceeds this many pixels; 0 disables export blocking.",
+    )
     return parser.parse_args(list(argv) if argv is not None else None)
 
 
@@ -536,6 +1460,16 @@ def main(argv: Iterable[str] | None = None) -> int:
     )
     true_cals = load_calibrations(case_dir, num_cams)
     start_cals = perturb_calibrations(true_cals, args.perturbation_scale)
+    reference_geometry_points = load_reference_geometry_points(case_dir, num_cams)
+    if args.geometry_guard_mode == "auto":
+        geometry_guard_mode = "hard" if reference_geometry_points is not None else "off"
+    else:
+        geometry_guard_mode = args.geometry_guard_mode
+    geometry_export_threshold = (
+        args.geometry_export_threshold if reference_geometry_points is not None else None
+    )
+    known_points = build_known_point_constraints(point_init, args.known_points)
+    known_point_sigmas = args.known_point_sigma if known_points else None
 
     output_dir = None if args.skip_write else args.output_dir.resolve()
     if output_dir is not None:
@@ -543,12 +1477,126 @@ def main(argv: Iterable[str] | None = None) -> int:
 
     print(f"Case: {case_dir}")
     print(f"Cameras: {num_cams}")
-    print(f"Observations: {observed_pixels.shape[0]} points across {observed_pixels.shape[1]} cameras")
+    print(
+        f"Observations: {observed_pixels.shape[0]} points across {observed_pixels.shape[1]} cameras"
+    )
+    if known_points:
+        print(
+            f"Known-point anchors: {len(known_points)} (sigma={args.known_point_sigma:.3f})"
+        )
+    else:
+        print("Known-point anchors: disabled")
+    if reference_geometry_points is not None:
+        print(
+            f"Geometry guard: mode={geometry_guard_mode}, guard_threshold={args.geometry_guard_threshold:.3f}px, export_threshold={args.geometry_export_threshold:.3f}px"
+        )
+    else:
+        print("Geometry guard: unavailable for this case (no known 3D target file found)")
     print(f"Output folders: {output_dir if output_dir is not None else 'disabled'}")
     print()
 
+    experiments = default_experiments(
+        known_points=known_points or None,
+        known_point_sigmas=known_point_sigmas,
+        perturbation_scale=args.perturbation_scale,
+        geometry_guard_mode=geometry_guard_mode,
+        geometry_guard_threshold=args.geometry_guard_threshold,
+    )
+
+    if args.diagnose_fixed_pairs:
+        spec = find_experiment_spec(experiments, args.diagnostic_experiment)
+        diagnostics = run_fixed_camera_pair_diagnostics(
+            spec,
+            observed_pixels=observed_pixels,
+            point_init=point_init,
+            control=control,
+            start_cals=start_cals,
+            reference_cals=true_cals,
+            reference_geometry_points=reference_geometry_points,
+            geometry_export_threshold=geometry_export_threshold,
+            source_case_dir=case_dir,
+        )
+        print(
+            f"Fixed-pair diagnostics for {spec.name}: {spec.description}"
+        )
+        print(format_fixed_camera_diagnostics(diagnostics))
+        print()
+        print("Anchor participation summary")
+        print(summarize_anchor_participation(diagnostics, num_cams))
+        return 0
+
+    if args.diagnose_epipolar or args.diagnose_quadruplets:
+        spec = find_experiment_spec(experiments, args.diagnostic_experiment)
+        diagnostic_start_cals = build_experiment_start_calibrations(
+            spec,
+            start_cals=start_cals,
+            reference_cals=true_cals,
+        )
+        diagnostic_result = run_experiment(
+            spec,
+            observed_pixels=observed_pixels,
+            point_init=point_init,
+            control=control,
+            start_cals=start_cals,
+            reference_cals=true_cals,
+            reference_geometry_points=reference_geometry_points,
+            geometry_export_threshold=geometry_export_threshold,
+            source_case_dir=case_dir,
+            output_dir=None,
+        )
+        if diagnostic_result.refined_cals is None:
+            raise ValueError("Diagnostic experiment did not return refined calibrations")
+
+        print(
+            f"Diagnostics for {spec.name}: {spec.description}"
+        )
+        print(
+            f"Result RMS: {diagnostic_result.final_rms:.6f} px, ray: {diagnostic_result.final_ray_convergence:.6f}, notes: {diagnostic_result.notes}"
+        )
+        print()
+
+        if args.diagnose_epipolar:
+            vpar = read_volume_par(case_dir / "parameters/criteria.par")
+            baseline_epipolar = summarize_epipolar_consistency(
+                observed_pixels,
+                diagnostic_start_cals,
+                control,
+                vpar,
+                num_curve_points=args.epipolar_curve_points,
+            )
+            final_epipolar = summarize_epipolar_consistency(
+                observed_pixels,
+                diagnostic_result.refined_cals,
+                control,
+                vpar,
+                num_curve_points=args.epipolar_curve_points,
+            )
+            print(f"Epipolar diagnostics against criteria.par eps0={vpar.eps0:.6f}")
+            print("Baseline")
+            print(format_epipolar_diagnostics(baseline_epipolar))
+            print()
+            print("Final")
+            print(format_epipolar_diagnostics(final_epipolar))
+            print()
+
+        if args.diagnose_quadruplets:
+            baseline_quadruplets = summarize_quadruplet_sensitivity(
+                observed_pixels,
+                diagnostic_start_cals,
+                control,
+            )
+            final_quadruplets = summarize_quadruplet_sensitivity(
+                observed_pixels,
+                diagnostic_result.refined_cals,
+                control,
+            )
+            print("Quadruplet leave-one-camera-out sensitivity")
+            print(format_quadruplet_sensitivity(baseline_quadruplets, final_quadruplets))
+
+        return 0
+
     results = []
-    for spec in default_experiments():
+    for spec in experiments:
         print(f"Running {spec.name}: {spec.description}")
         result = run_experiment(
             spec,
@@ -556,6 +1604,9 @@ def main(argv: Iterable[str] | None = None) -> int:
             point_init=point_init,
             control=control,
             start_cals=start_cals,
+            reference_cals=true_cals,
+            reference_geometry_points=reference_geometry_points,
+            geometry_export_threshold=geometry_export_threshold,
             source_case_dir=case_dir,
             output_dir=output_dir,
         )
@@ -570,4 +1621,4 @@ def main(argv: Iterable[str] | None = None) -> int:
 
 
 if __name__ == "__main__":
-    raise SystemExit(main())
\ No newline at end of file
+    raise SystemExit(main())
diff --git a/openptv_python/generate_synthetic_cavity_case.py b/openptv_python/generate_synthetic_cavity_case.py
new file mode 100644
index 0000000..585d520
--- /dev/null
+++ b/openptv_python/generate_synthetic_cavity_case.py
@@ -0,0 +1,518 @@
+"""Generate a deterministic synthetic case modeled after test_cavity."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import shutil
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Iterable, List, Sequence, cast
+
+import numpy as np
+
+from .calibration import Calibration, read_calibration, write_calibration
+from .imgcoord import image_coordinates
+from .orientation import (
+    external_calibration,
+    full_calibration,
+    initialize_bundle_adjustment_points,
+    match_detection_to_ref,
+)
+from .parameters import ControlPar, OrientPar, VolumePar, read_volume_par
+from .sortgrid import read_sortgrid_par
+from .tracking_frame_buf import Pathinfo, Target, n_tupel_dtype, write_path_frame, write_targets
+from .trafo import arr_metric_to_pixel
+
+DEFAULT_SOURCE_CASE = Path("tests/testing_fodder/test_cavity")
+DEFAULT_OUTPUT_CASE = Path("tests/testing_fodder/test_cavity_synthetic")
+DEFAULT_SEED = 20260306
+FRAME_NUMBERS = (10001, 10002)
+
+
+@dataclass(frozen=True)
+class SyntheticCaseSummary:
+    """Metadata describing the generated synthetic case."""
+
+    seed: int
+    num_calibration_points: int
+    num_frames: int
+    particles_per_frame: int
+    calibration_position_errors: List[float]
+    calibration_angle_errors: List[float]
+
+
+def clone_calibration(cal: Calibration) -> Calibration:
+    """Return a detached calibration copy."""
+    return Calibration(
+        ext_par=cal.ext_par.copy(),
+        int_par=cal.int_par.copy(),
+        glass_par=cal.glass_par.copy(),
+        added_par=cal.added_par.copy(),
+        mmlut=cal.mmlut,
+        mmlut_data=cal.mmlut_data,
+    )
+
+
+def make_target(x: float, y: float, pnr: int) -> Target:
+    """Create a simple synthetic target record."""
+    return Target(
+        pnr=pnr,
+        x=float(x),
+        y=float(y),
+        n=21,
+        nx=5,
+        ny=5,
+        sumg=5000,
+        tnr=-1,
+    )
+
+
+def camera_points_in_bounds(pixel_points: np.ndarray, cpar: ControlPar, margin: float = 8.0) -> np.ndarray:
+    """Return a mask of points lying safely inside the sensor bounds."""
+    return (
+        (pixel_points[:, 0] >= margin)
+        & (pixel_points[:, 0] <= cpar.imx - margin)
+        & (pixel_points[:, 1] >= margin)
+        & (pixel_points[:, 1] <= cpar.imy - margin)
+    )
+
+
+def z_bounds_at_x(x_coord: float, vpar: VolumePar) -> tuple[float, float]:
+    """Interpolate the admissible z range for one x coordinate."""
+    x0, x1 = vpar.x_lay
+    zmin0, zmin1 = vpar.z_min_lay
+    zmax0, zmax1 = vpar.z_max_lay
+    if x1 == x0:
+        return zmin0, zmax0
+    weight = (x_coord - x0) / (x1 - x0)
+    z_min = zmin0 + weight * (zmin1 - zmin0)
+    z_max = zmax0 + weight * (zmax1 - zmax0)
+    return float(z_min), float(z_max)
+
+
+def project_pixels(points_3d: np.ndarray, cals: Sequence[Calibration], cpar: ControlPar) -> np.ndarray:
+    """Project 3D points into all cameras in pixel coordinates."""
+    observed = np.empty((points_3d.shape[0], len(cals), 2), dtype=np.float64)
+    for cam_index, cal in enumerate(cals):
+        observed[:, cam_index, :] = arr_metric_to_pixel(
+            image_coordinates(points_3d, cal, cpar.mm),
+            cpar,
+        )
+    return observed
+
+
+def select_visible_points(
+    candidate_points: np.ndarray,
+    cals: Sequence[Calibration],
+    cpar: ControlPar,
+    count: int,
+) -> np.ndarray:
+    """Return the first points that project inside every camera image."""
+    projected = project_pixels(candidate_points, cals, cpar)
+    visibility = np.ones(candidate_points.shape[0], dtype=bool)
+    for cam_index in range(len(cals)):
+        visibility &= camera_points_in_bounds(projected[:, cam_index, :], cpar)
+    visible_points = candidate_points[visibility]
+    if visible_points.shape[0] < count:
+        raise ValueError("Not enough visible points for the requested synthetic case")
+    return visible_points[:count]
+
+
+def build_calibration_body(vpar: VolumePar, cals: Sequence[Calibration], cpar: ControlPar) -> np.ndarray:
+    """Build a structured 3D calibration body visible in all cameras."""
+    xs = np.linspace(vpar.x_lay[0] + 4.0, vpar.x_lay[1] - 4.0, 8)
+    ys = np.linspace(-16.0, 16.0, 6)
+    candidates = []
+    for x_coord in xs:
+        z_min, z_max = z_bounds_at_x(float(x_coord), vpar)
+        if z_max - z_min < 10.0:
+            continue
+        zs = np.linspace(z_min + 4.0, z_max - 4.0, 4)
+        for y_coord in ys:
+            for z_coord in zs:
+                candidates.append([float(x_coord), float(y_coord), float(z_coord)])
+    candidate_points = np.asarray(candidates, dtype=np.float64)
+    return select_visible_points(candidate_points, cals, cpar, count=48)
+
+
+def generate_particle_cloud(
+    rng: np.random.Generator,
+    vpar: VolumePar,
+    cals: Sequence[Calibration],
+    cpar: ControlPar,
+    count: int,
+) -> np.ndarray:
+    """Sample random 3D particles inside the observed volume and keep visible quadruplets."""
+    accepted: List[np.ndarray] = []
+    while len(accepted) < count:
+        x_coord = float(rng.uniform(vpar.x_lay[0] + 2.0, vpar.x_lay[1] - 2.0))
+        z_min, z_max = z_bounds_at_x(x_coord, vpar)
+        y_coord = float(rng.uniform(-18.0, 18.0))
+        z_coord = float(rng.uniform(z_min + 2.0, z_max - 2.0))
+        point = np.asarray([[x_coord, y_coord, z_coord]], dtype=np.float64)
+        projected = project_pixels(point, cals, cpar)[0]
+        if all(camera_points_in_bounds(projected[None, cam_index, :], cpar)[0] for cam_index in range(len(cals))):
+            accepted.append(point[0])
+    return np.asarray(accepted, dtype=np.float64)
+
+
+def shuffled_targets_from_pixels(
+    pixel_points: np.ndarray,
+    rng: np.random.Generator,
+    noise_sigma: float,
+) -> tuple[List[Target], np.ndarray]:
+    """Shuffle projected pixels into a target list and return point-to-target indices."""
+    noisy_pixels = pixel_points + rng.normal(0.0, noise_sigma, size=pixel_points.shape)
+    order = rng.permutation(pixel_points.shape[0])
+    targets = [make_target(noisy_pixels[target_index, 0], noisy_pixels[target_index, 1], list_index) for list_index, target_index in enumerate(order)]
+    point_to_target = np.empty(pixel_points.shape[0], dtype=np.int32)
+    for list_index, target_index in enumerate(order):
+        point_to_target[target_index] = list_index
+    return targets, point_to_target
+
+
+def write_calibration_body_points(points: np.ndarray, output_file: Path) -> None:
+    """Write a calblock-compatible calibration body file."""
+    lines = [f"{index + 1:11d}{point[0]:11.3f}{point[1]:11.3f}{point[2]:11.3f}" for index, point in enumerate(points)]
+    output_file.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def perturb_calibration_for_recovery(cal: Calibration, camera_index: int) -> Calibration:
+    """Create a deterministic seed calibration for full_calibration recovery."""
+    trial = clone_calibration(cal)
+    position_deltas = [
+        np.array([1.0, -0.8, 0.4]),
+        np.array([-0.7, 0.5, -0.3]),
+        np.array([0.6, 0.4, -0.5]),
+        np.array([-0.5, -0.6, 0.4]),
+    ]
+    angle_deltas = [
+        np.array([0.006, -0.004, 0.003]),
+        np.array([-0.005, 0.004, -0.003]),
+        np.array([0.004, 0.003, -0.002]),
+        np.array([-0.004, -0.003, 0.002]),
+    ]
+    trial.set_pos(trial.get_pos() + position_deltas[camera_index])
+    trial.set_angles(trial.get_angles() + angle_deltas[camera_index])
+    return trial
+
+
+def select_external_seed_subset(ref_points: np.ndarray) -> np.ndarray:
+    """Pick a well-spread subset of reference points for external calibration."""
+    mins = np.argmin(ref_points, axis=0)
+    maxs = np.argmax(ref_points, axis=0)
+    center = np.argmin(np.linalg.norm(ref_points - np.mean(ref_points, axis=0), axis=1))
+    indices = []
+    for index in np.concatenate([mins, maxs, np.array([center])]):
+        if int(index) not in indices:
+            indices.append(int(index))
+    return np.asarray(indices[:6], dtype=np.int32)
+
+
+def recover_calibrations_from_body(
+    truth_cals: Sequence[Calibration],
+    ref_points: np.ndarray,
+    calibration_targets_dir: Path,
+    output_cal_dir: Path,
+    truth_cal_dir: Path,
+    cpar: ControlPar,
+    orient_par: OrientPar,
+    sortgrid_eps: int,
+    rng: np.random.Generator,
+) -> tuple[List[Calibration], List[float], List[float]]:
+    """Recover working calibrations from synthetic calibration-body targets."""
+    recovered = []
+    position_errors = []
+    angle_errors = []
+
+    seed_indices = select_external_seed_subset(ref_points)
+
+    for camera_index, truth_cal in enumerate(truth_cals, start=1):
+        projected = arr_metric_to_pixel(image_coordinates(ref_points, truth_cal, cpar.mm), cpar)
+        targets, _ = shuffled_targets_from_pixels(projected, rng, noise_sigma=0.0)
+        write_targets(
+            targets,
+            len(targets),
+            str(calibration_targets_dir / f"cam{camera_index}.%05d"),
+            1,
+        )
+
+        seed = perturb_calibration_for_recovery(truth_cal, camera_index - 1)
+        external_calibration(
+            seed,
+            ref_points[seed_indices],
+            projected[seed_indices],
+            cpar,
+        )
+        sorted_targets = match_detection_to_ref(seed, ref_points, targets, cpar, sortgrid_eps)
+        full_calibration(
+            seed,
+            ref_points,
+            cast(np.ndarray, sorted_targets),
+            cpar,
+            orient_par,
+        )
+        recovered.append(seed)
+
+        write_calibration(
+            seed,
+            output_cal_dir / f"cam{camera_index}.tif.ori",
+            output_cal_dir / f"cam{camera_index}.tif.addpar",
+        )
+        write_calibration(
+            truth_cal,
+            truth_cal_dir / f"cam{camera_index}.tif.ori",
+            truth_cal_dir / f"cam{camera_index}.tif.addpar",
+        )
+
+        position_errors.append(float(np.linalg.norm(seed.get_pos() - truth_cal.get_pos())))
+        angle_errors.append(float(np.linalg.norm(seed.get_angles() - truth_cal.get_angles())))
+
+    return recovered, position_errors, angle_errors
+
+
+def build_frame_targets_and_paths(
+    frame_points: np.ndarray,
+    cals: Sequence[Calibration],
+    cpar: ControlPar,
+    rng: np.random.Generator,
+) -> tuple[list[list[Target]], np.recarray, list[Pathinfo]]:
+    """Create target files plus rt_is-compatible correspondences for one frame."""
+    projected = project_pixels(frame_points, cals, cpar)
+    per_camera_targets = []
+    per_camera_mapping = []
+    for cam_index in range(len(cals)):
+        targets, point_to_target = shuffled_targets_from_pixels(projected[:, cam_index, :], rng, noise_sigma=0.08)
+        per_camera_targets.append(targets)
+        per_camera_mapping.append(point_to_target)
+
+    observed_pixels = np.empty_like(projected)
+    for point_index in range(frame_points.shape[0]):
+        for cam_index in range(len(cals)):
+            target_index = per_camera_mapping[cam_index][point_index]
+            observed_pixels[point_index, cam_index, 0] = per_camera_targets[cam_index][target_index].x
+            observed_pixels[point_index, cam_index, 1] = per_camera_targets[cam_index][target_index].y
+
+    initial_points, _ = initialize_bundle_adjustment_points(observed_pixels, list(cals), cpar)
+    cor_buf = np.recarray((frame_points.shape[0],), dtype=n_tupel_dtype)
+    path_buf = [Pathinfo() for _ in range(frame_points.shape[0])]
+    for point_index in range(frame_points.shape[0]):
+        cor_buf[point_index].p = np.array(
+            [per_camera_mapping[cam_index][point_index] for cam_index in range(len(cals))],
+            dtype=np.int32,
+        )
+        cor_buf[point_index].corr = 1.0
+        path_buf[point_index].x = initial_points[point_index]
+    return per_camera_targets, cor_buf, path_buf
+
+
+def write_sequence_file(sequence_path: Path, frame_numbers: Sequence[int]) -> None:
+    """Write a sequence.par file consistent with img_orig target files."""
+    lines = [
+        "img_orig/cam1.%05d",
+        "img_orig/cam2.%05d",
+        "img_orig/cam3.%05d",
+        "img_orig/cam4.%05d",
+        str(frame_numbers[0]),
+        str(frame_numbers[-1]),
+    ]
+    sequence_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def prepare_output_case(source_case: Path, output_case: Path) -> None:
+    """Copy the source structure and clear generated data directories."""
+    shutil.copytree(source_case, output_case, dirs_exist_ok=True)
+    for name in ("cal", "img_orig", "res_orig", "ground_truth", "calibration_targets"):
+        path = output_case / name
+        if path.exists():
+            shutil.rmtree(path)
+    (output_case / "cal").mkdir(parents=True, exist_ok=True)
+    (output_case / "img_orig").mkdir(parents=True, exist_ok=True)
+    (output_case / "res_orig").mkdir(parents=True, exist_ok=True)
+    (output_case / "ground_truth" / "cal").mkdir(parents=True, exist_ok=True)
+    (output_case / "ground_truth" / "particles").mkdir(parents=True, exist_ok=True)
+    (output_case / "calibration_targets").mkdir(parents=True, exist_ok=True)
+
+
+def write_case_readme(output_case: Path) -> None:
+    """Document the generated synthetic case contents."""
+    text = """# Synthetic Cavity Case
+
+This case is generated deterministically from the geometry of `test_cavity`, but all observations come from known ground truth.
+
+Contents:
+
+- `cal/`: working calibrations recovered from synthetic calibration-body targets using `full_calibration`.
+- `ground_truth/cal/`: exact camera models used to project the synthetic data.
+- `ground_truth/calibration_body_points.txt`: known 3D calibration-body points.
+- `calibration_targets/`: synthetic target files for that calibration body.
+- `img_orig/`: synthetic particle target files for two frames.
+- `res_orig/`: synthetic `rt_is`, `ptv_is`, and `added` files for those frames.
+- `ground_truth/particles/`: exact 3D particle coordinates per frame.
+- `ground_truth/manifest.json`: generation seed and calibration-recovery errors.
+"""
+    (output_case / "README.md").write_text(text, encoding="utf-8")
+
+
+def generate_synthetic_case(
+    source_case: Path,
+    output_case: Path,
+    *,
+    seed: int,
+    particles_per_frame: int,
+) -> SyntheticCaseSummary:
+    """Generate the synthetic cavity-like case on disk."""
+    rng = np.random.default_rng(seed)
+    prepare_output_case(source_case, output_case)
+    write_case_readme(output_case)
+
+    control = ControlPar(4).from_file(source_case / "parameters/ptv.par")
+    vpar = read_volume_par(source_case / "parameters/criteria.par")
+    orient_par = OrientPar().from_file(source_case / "parameters/orient.par")
+    sortgrid_eps = read_sortgrid_par(source_case / "parameters/sortgrid.par")
+    truth_cals = [
+        read_calibration(
+            source_case / f"cal/cam{camera_index}.tif.ori",
+            source_case / f"cal/cam{camera_index}.tif.addpar",
+        )
+        for camera_index in range(1, 5)
+    ]
+
+    calibration_body = build_calibration_body(vpar, truth_cals, control)
+    write_calibration_body_points(
+        calibration_body,
+        output_case / "ground_truth/calibration_body_points.txt",
+    )
+    shutil.copy2(
+        output_case / "ground_truth/calibration_body_points.txt",
+        output_case / "cal/calblock.txt",
+    )
+
+    recovered_cals, position_errors, angle_errors = recover_calibrations_from_body(
+        truth_cals,
+        calibration_body,
+        output_case / "calibration_targets",
+        output_case / "cal",
+        output_case / "ground_truth/cal",
+        control,
+        orient_par,
+        sortgrid_eps,
+        rng,
+    )
+
+    write_sequence_file(output_case / "parameters/sequence.par", FRAME_NUMBERS)
+
+    for frame_number in FRAME_NUMBERS:
+        frame_points = generate_particle_cloud(
+            rng,
+            vpar,
+            truth_cals,
+            control,
+            particles_per_frame,
+        )
+        np.savetxt(
+            output_case / "ground_truth/particles" / f"frame_{frame_number}.txt",
+            frame_points,
+            fmt="%.6f",
+            header="x y z",
+            comments="",
+        )
+
+        per_camera_targets, cor_buf, path_buf = build_frame_targets_and_paths(
+            frame_points,
+            recovered_cals,
+            control,
+            rng,
+        )
+        for camera_index, targets in enumerate(per_camera_targets, start=1):
+            write_targets(
+                targets,
+                len(targets),
+                str(output_case / "img_orig" / f"cam{camera_index}.%05d"),
+                frame_number,
+            )
+
+        write_path_frame(
+            cor_buf,
+            path_buf,
+            len(path_buf),
+            str(output_case / "res_orig" / "rt_is"),
+            str(output_case / "res_orig" / "ptv_is"),
+            str(output_case / "res_orig" / "added"),
+            frame_number,
+        )
+
+    manifest = SyntheticCaseSummary(
+        seed=seed,
+        num_calibration_points=int(calibration_body.shape[0]),
+        num_frames=len(FRAME_NUMBERS),
+        particles_per_frame=particles_per_frame,
+        calibration_position_errors=position_errors,
+        calibration_angle_errors=angle_errors,
+    )
+    (output_case / "ground_truth/manifest.json").write_text(
+        json.dumps(manifest.__dict__, indent=2) + "\n",
+        encoding="utf-8",
+    )
+    return manifest
+
+
+def parse_args(argv: Iterable[str] | None = None) -> argparse.Namespace:
+    """Parse command-line arguments for synthetic-case generation."""
+    parser = argparse.ArgumentParser(
+        description="Generate a synthetic bundle-adjustment case modeled after test_cavity.",
+    )
+    parser.add_argument(
+        "--source-case",
+        type=Path,
+        default=DEFAULT_SOURCE_CASE,
+        help="Empirical case used as the geometric template.",
+    )
+    parser.add_argument(
+        "--output-case",
+        type=Path,
+        default=DEFAULT_OUTPUT_CASE,
+        help="Destination case folder to populate.",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=DEFAULT_SEED,
+        help="Random seed for deterministic particle generation.",
+    )
+    parser.add_argument(
+        "--particles-per-frame",
+        type=int,
+        default=96,
+        help="Number of fully observed particles to synthesize per frame.",
+    )
+    return parser.parse_args(list(argv) if argv is not None else None)
+
+
+def main(argv: Iterable[str] | None = None) -> int:
+    """Generate the synthetic case and print a compact summary."""
+    args = parse_args(argv)
+    summary = generate_synthetic_case(
+        args.source_case.resolve(),
+        args.output_case.resolve(),
+        seed=args.seed,
+        particles_per_frame=args.particles_per_frame,
+    )
+    print(f"Wrote synthetic case to {args.output_case.resolve()}")
+    print(f"Calibration points: {summary.num_calibration_points}")
+    print(f"Frames: {summary.num_frames}")
+    print(f"Particles per frame: {summary.particles_per_frame}")
+    print(
+        "Calibration recovery position errors: "
+        + ", ".join(f"{value:.6f}" for value in summary.calibration_position_errors)
+    )
+    print(
+        "Calibration recovery angle errors: "
+        + ", ".join(f"{value:.6f}" for value in summary.calibration_angle_errors)
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
\ No newline at end of file
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index b86c0ad..0e22115 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -1369,11 +1369,15 @@ def _bundle_adjustment_jacobian_sparsity(
     point_offset: int,
     optimize_points: bool,
     active_priors: List[Tuple[int, int, float, float]],
+    active_known_points: List[Tuple[int, np.ndarray, np.ndarray]],
 ) -> scipy.sparse.csr_matrix:
     """Build the Jacobian sparsity pattern for finite-difference bundle adjustment."""
     num_observation_residuals = int(np.count_nonzero(obs_mask) * 2)
     num_prior_residuals = len(active_priors)
-    total_residuals = num_observation_residuals + num_prior_residuals
+    num_known_point_residuals = len(active_known_points) * 3
+    total_residuals = (
+        num_observation_residuals + num_prior_residuals + num_known_point_residuals
+    )
     total_parameters = point_offset + (obs_mask.shape[0] * 3 if optimize_points else 0)
 
     sparsity = scipy.sparse.lil_matrix(
@@ -1401,14 +1405,61 @@ def _bundle_adjustment_jacobian_sparsity(
 
             row += 2
 
-    for cam_index, param_index, _sigma, _base_value in active_priors:
+    for cam_index, param_index, _prior_sigma, _base_value in active_priors:
         cam_start = cam_index * camera_block_size
         sparsity[row, cam_start + param_index] = 1
         row += 1
 
+    if optimize_points:
+        for point_index, _target, _point_sigma in active_known_points:
+            point_start = point_offset + point_index * 3
+            sparsity[row : row + 3, point_start : point_start + 3] = 1
+            row += 3
+
     return sparsity.tocsr()
 
 
+def _normalize_known_point_constraints(
+    num_points: int,
+    known_points: Optional[Dict[int, np.ndarray]],
+    known_point_sigmas: Optional[float | np.ndarray],
+) -> List[Tuple[int, np.ndarray, np.ndarray]]:
+    """Normalize known-point priors into indexed target and sigma vectors."""
+    if known_points is None:
+        return []
+
+    sigma_source: float | np.ndarray
+    sigma_source = 1.0 if known_point_sigmas is None else known_point_sigmas
+    sigma_array = np.asarray(sigma_source, dtype=np.float64)
+
+    constraints = []
+    for point_index in sorted(known_points):
+        if point_index < 0 or point_index >= num_points:
+            raise ValueError("known_points contains an out-of-range point index")
+
+        target = np.asarray(known_points[point_index], dtype=np.float64)
+        if target.shape != (3,):
+            raise ValueError("Each known_points entry must have shape (3,)")
+
+        if sigma_array.ndim == 0:
+            sigma = np.full(3, float(sigma_array), dtype=np.float64)
+        elif sigma_array.shape == (3,):
+            sigma = sigma_array.copy()
+        elif sigma_array.shape == (num_points, 3):
+            sigma = sigma_array[point_index].copy()
+        else:
+            raise ValueError(
+                "known_point_sigmas must be a scalar, shape (3,), or shape (num_points, 3)"
+            )
+
+        if np.any(sigma <= 0):
+            raise ValueError("known_point_sigmas must be strictly positive")
+
+        constraints.append((point_index, target, sigma))
+
+    return constraints
+
+
 def multi_camera_bundle_adjustment(
     observed_pixels: np.ndarray,
     cals: List[Calibration],
@@ -1425,6 +1476,8 @@ def multi_camera_bundle_adjustment(
     max_nfev: Optional[int] = None,
     optimize_extrinsics: bool = True,
     optimize_points: bool = True,
+    known_points: Optional[Dict[int, np.ndarray]] = None,
+    known_point_sigmas: Optional[float | np.ndarray] = None,
     ftol: Optional[float] = None,
     xtol: Optional[float] = None,
     gtol: Optional[float] = None,
@@ -1474,6 +1527,10 @@ def multi_camera_bundle_adjustment(
         raise ValueError(
             "Bundle adjustment must optimize points or at least one camera"
         )
+    if known_points is not None and not optimize_points:
+        raise ValueError(
+            "known_points constraints require optimize_points=True in the current implementation"
+        )
 
     # Refining both 3D points and camera poses from image observations has a similarity
     # gauge. One fixed camera removes global translation/rotation, but scale still drifts
@@ -1551,6 +1608,12 @@ def multi_camera_bundle_adjustment(
                 continue
             active_priors.append((cam_index, param_index, sigma, base_value))
 
+    active_known_points = _normalize_known_point_constraints(
+        num_points,
+        known_points,
+        known_point_sigmas,
+    )
+
     camera_lower, camera_upper = _expand_parameter_limits(
         parameter_names,
         parameter_bounds,
@@ -1592,7 +1655,11 @@ def unpack_parameters(params: np.ndarray) -> Tuple[List[Calibration], np.ndarray
 
     def residual_vector(params: np.ndarray) -> np.ndarray:
         trial_cals, points = unpack_parameters(params)
-        residuals = np.empty(int(np.count_nonzero(obs_mask) * 2) + len(active_priors))
+        residuals = np.empty(
+            int(np.count_nonzero(obs_mask) * 2)
+            + len(active_priors)
+            + 3 * len(active_known_points)
+        )
         row = 0
 
         for cam, point_indices in enumerate(observation_point_indices):
@@ -1615,6 +1682,10 @@ def residual_vector(params: np.ndarray) -> np.ndarray:
             residuals[row] = (value - base_value) / sigma
             row += 1
 
+        for point_index, target, point_sigma in active_known_points:
+            residuals[row : row + 3] = (points[point_index] - target) / point_sigma
+            row += 3
+
         return residuals
 
     initial_cals, initial_points = unpack_parameters(x0)
@@ -1639,6 +1710,7 @@ def residual_vector(params: np.ndarray) -> np.ndarray:
             point_offset,
             optimize_points,
             active_priors,
+            active_known_points,
         )
     if ftol is not None:
         least_squares_kwargs["ftol"] = ftol
@@ -1663,6 +1735,9 @@ def residual_vector(params: np.ndarray) -> np.ndarray:
         observed_pixels, refined_points, refined_cals, cpar
     )
     result["optimized_camera_indices"] = optimized_cam_indices
+    result["known_point_indices"] = [
+        point_index for point_index, _, _ in active_known_points
+    ]
 
     return refined_cals, refined_points, result
 
@@ -1691,10 +1766,80 @@ def guarded_two_step_bundle_adjustment(
     intrinsic_gtol: Optional[float] = 1e-12,
     pose_optimize_points: bool = True,
     intrinsic_optimize_points: bool = True,
+    known_points: Optional[Dict[int, np.ndarray]] = None,
+    known_point_sigmas: Optional[float | np.ndarray] = None,
+    geometry_reference_points: Optional[np.ndarray] = None,
+    geometry_reference_cals: Optional[List[Calibration]] = None,
+    geometry_guard_mode: str = "off",
+    geometry_guard_threshold: Optional[float] = None,
     reject_worse_solution: bool = True,
     reject_on_ray_convergence: bool = True,
 ) -> Tuple[List[Calibration], np.ndarray, Dict[str, object]]:
     """Run pose-only BA then tightly constrained intrinsics BA with acceptance checks."""
+
+    def projection_drift_summaries(
+        reference_cals: List[Calibration],
+        candidate_cals: List[Calibration],
+        reference_points: Optional[np.ndarray],
+    ) -> Optional[List[Dict[str, float]]]:
+        if reference_points is None:
+            return None
+
+        summaries: List[Dict[str, float]] = []
+        for camera_index, (reference_cal, candidate_cal) in enumerate(
+            zip(reference_cals, candidate_cals),
+            start=1,
+        ):
+            reference_pixels = []
+            candidate_pixels = []
+            for point in reference_points:
+                ref_x, ref_y = img_coord(point, reference_cal, cpar.mm)
+                cand_x, cand_y = img_coord(point, candidate_cal, cpar.mm)
+                reference_pixels.append(metric_to_pixel(ref_x, ref_y, cpar))
+                candidate_pixels.append(metric_to_pixel(cand_x, cand_y, cpar))
+
+            displacement = np.linalg.norm(
+                np.asarray(candidate_pixels) - np.asarray(reference_pixels),
+                axis=1,
+            )
+            summaries.append(
+                {
+                    "camera_index": float(camera_index),
+                    "mean_distance": float(displacement.mean()),
+                    "p95_distance": float(np.percentile(displacement, 95.0)),
+                    "max_distance": float(displacement.max()),
+                }
+            )
+
+        return summaries
+
+    def max_projection_drift(
+        summaries: Optional[List[Dict[str, float]]],
+    ) -> Optional[float]:
+        if not summaries:
+            return None
+        return max(item["max_distance"] for item in summaries)
+
+    def geometry_stage_ok(
+        candidate_metric: Optional[float],
+        baseline_metric: Optional[float],
+    ) -> bool:
+        if geometry_guard_mode == "off" or candidate_metric is None:
+            return True
+        if geometry_guard_mode == "soft":
+            if baseline_metric is None:
+                return True
+            return candidate_metric <= baseline_metric + 1e-12
+        if geometry_guard_mode == "hard":
+            if geometry_guard_threshold is None or geometry_guard_threshold <= 0:
+                raise ValueError(
+                    "geometry_guard_threshold must be positive when geometry_guard_mode='hard'"
+                )
+            return candidate_metric <= geometry_guard_threshold
+        raise ValueError(
+            "geometry_guard_mode must be one of 'off', 'soft', or 'hard'"
+        )
+
     base_cals = [_clone_calibration(cal) for cal in cals]
     if point_init is None:
         base_points, _ = initialize_bundle_adjustment_points(
@@ -1707,6 +1852,15 @@ def guarded_two_step_bundle_adjustment(
 
     baseline_rms = reprojection_rms(observed_pixels, base_points, base_cals, cpar)
     baseline_ray_convergence = mean_ray_convergence(observed_pixels, base_cals, cpar)
+    if geometry_reference_cals is None:
+        geometry_reference_cals = [_clone_calibration(cal) for cal in base_cals]
+
+    baseline_geometry = projection_drift_summaries(
+        geometry_reference_cals,
+        base_cals,
+        geometry_reference_points,
+    )
+    baseline_geometry_max = max_projection_drift(baseline_geometry)
 
     pose_cals, pose_points, pose_result = multi_camera_bundle_adjustment(
         observed_pixels,
@@ -1722,9 +1876,17 @@ def guarded_two_step_bundle_adjustment(
         max_nfev=pose_max_nfev,
         optimize_extrinsics=True,
         optimize_points=pose_optimize_points,
+        known_points=known_points,
+        known_point_sigmas=known_point_sigmas,
     )
     pose_rms = reprojection_rms(observed_pixels, pose_points, pose_cals, cpar)
     pose_ray_convergence = mean_ray_convergence(observed_pixels, pose_cals, cpar)
+    pose_geometry = projection_drift_summaries(
+        geometry_reference_cals,
+        pose_cals,
+        geometry_reference_points,
+    )
+    pose_geometry_max = max_projection_drift(pose_geometry)
 
     intrinsic_fixed = list(range(len(cals)))
     intrinsic_cals, intrinsic_points, intrinsic_result = multi_camera_bundle_adjustment(
@@ -1741,6 +1903,8 @@ def guarded_two_step_bundle_adjustment(
         max_nfev=intrinsic_max_nfev,
         optimize_extrinsics=False,
         optimize_points=intrinsic_optimize_points,
+        known_points=known_points,
+        known_point_sigmas=known_point_sigmas,
         ftol=intrinsic_ftol,
         xtol=intrinsic_xtol,
         gtol=intrinsic_gtol,
@@ -1751,6 +1915,12 @@ def guarded_two_step_bundle_adjustment(
     intrinsic_ray_convergence = mean_ray_convergence(
         observed_pixels, intrinsic_cals, cpar
     )
+    intrinsic_geometry = projection_drift_summaries(
+        geometry_reference_cals,
+        intrinsic_cals,
+        geometry_reference_points,
+    )
+    intrinsic_geometry_max = max_projection_drift(intrinsic_geometry)
 
     accepted_stage = "intrinsics"
     final_cals = intrinsic_cals
@@ -1762,10 +1932,17 @@ def guarded_two_step_bundle_adjustment(
         not reject_on_ray_convergence
         or pose_ray_convergence <= baseline_ray_convergence
     )
+    pose_geometry_ok = geometry_stage_ok(pose_geometry_max, baseline_geometry_max)
+    pose_ok = pose_ok and pose_geometry_ok
     intrinsic_ok = intrinsic_rms <= pose_rms and (
         not reject_on_ray_convergence
         or intrinsic_ray_convergence <= pose_ray_convergence
     )
+    intrinsic_geometry_ok = geometry_stage_ok(
+        intrinsic_geometry_max,
+        pose_geometry_max,
+    )
+    intrinsic_ok = intrinsic_ok and intrinsic_geometry_ok
 
     if reject_worse_solution:
         if not pose_ok:
@@ -1786,14 +1963,24 @@ def guarded_two_step_bundle_adjustment(
         "baseline_mean_ray_convergence": baseline_ray_convergence,
         "baseline_cals": base_cals,
         "baseline_points": base_points,
+        "baseline_geometry": baseline_geometry,
+        "baseline_geometry_max": baseline_geometry_max,
         "pose_reprojection_rms": pose_rms,
         "pose_mean_ray_convergence": pose_ray_convergence,
         "pose_cals": pose_cals,
         "pose_points": pose_points,
+        "pose_geometry": pose_geometry,
+        "pose_geometry_max": pose_geometry_max,
+        "pose_geometry_ok": pose_geometry_ok,
         "intrinsic_reprojection_rms": intrinsic_rms,
         "intrinsic_mean_ray_convergence": intrinsic_ray_convergence,
         "intrinsic_cals": intrinsic_cals,
         "intrinsic_points": intrinsic_points,
+        "intrinsic_geometry": intrinsic_geometry,
+        "intrinsic_geometry_max": intrinsic_geometry_max,
+        "intrinsic_geometry_ok": intrinsic_geometry_ok,
+        "geometry_guard_mode": geometry_guard_mode,
+        "geometry_guard_threshold": geometry_guard_threshold,
         "accepted_stage": accepted_stage,
         "final_reprojection_rms": final_rms,
         "final_mean_ray_convergence": final_ray_convergence,
diff --git a/tests/test_bundle_adjustment.py b/tests/test_bundle_adjustment.py
index 2ca4d70..fad2c68 100644
--- a/tests/test_bundle_adjustment.py
+++ b/tests/test_bundle_adjustment.py
@@ -237,6 +237,171 @@ def test_bundle_adjustment_rejects_scale_ambiguous_configuration(self):
                 max_nfev=10,
             )
 
+    def test_multi_camera_bundle_adjustment_known_points_constrain_geometry(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+                [0.0, 0.0, 3.0],
+                [6.0, -4.0, -2.0],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        point_init = points.copy()
+        point_init[:2] += np.array([[3.0, -2.0, 1.5], [-2.5, 1.0, -1.0]])
+        start_cals = self.perturb_calibrations(self.true_cals)
+        ba_kwargs = {
+            "point_init": point_init,
+            "fixed_camera_indices": [0, 1],
+            "loss": "linear",
+            "method": "trf",
+            "prior_sigmas": {
+                "x0": 1.0,
+                "y0": 1.0,
+                "z0": 1.0,
+                "omega": 0.01,
+                "phi": 0.01,
+                "kappa": 0.01,
+            },
+            "max_nfev": 8,
+        }
+
+        unconstrained_cals, unconstrained_points, unconstrained_result = (
+            multi_camera_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                **ba_kwargs,
+            )
+        )
+        constrained_cals, constrained_points, constrained_result = (
+            multi_camera_bundle_adjustment(
+                observed_pixels,
+                self.perturb_calibrations(self.true_cals),
+                self.control,
+                OrientPar(),
+                known_points={0: points[0], 1: points[1]},
+                known_point_sigmas=1e-3,
+                **ba_kwargs,
+            )
+        )
+
+        unconstrained_error = float(
+            np.mean(np.linalg.norm(unconstrained_points[:2] - points[:2], axis=1))
+        )
+        constrained_error = float(
+            np.mean(np.linalg.norm(constrained_points[:2] - points[:2], axis=1))
+        )
+
+        self.assertLess(constrained_error, unconstrained_error)
+        self.assertLess(constrained_error, 1e-2)
+        self.assertIn(0, constrained_result["known_point_indices"])
+        self.assertIn(1, constrained_result["known_point_indices"])
+        self.assertLess(
+            reprojection_rms(
+                observed_pixels,
+                constrained_points,
+                constrained_cals,
+                self.control,
+            ),
+            unconstrained_result["initial_reprojection_rms"],
+        )
+
+    def test_fixed_camera_indices_preserve_selected_camera_poses(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+                [0.0, 0.0, 3.0],
+                [6.0, -4.0, -2.0],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        start_cals = self.perturb_calibrations(self.true_cals)
+        fixed_indices = [0, 1]
+        refined_cals, _, result = multi_camera_bundle_adjustment(
+            observed_pixels,
+            start_cals,
+            self.control,
+            OrientPar(),
+            point_init=points,
+            fixed_camera_indices=fixed_indices,
+            loss="linear",
+            method="lm",
+            prior_sigmas={
+                "x0": 1.0,
+                "y0": 1.0,
+                "z0": 1.0,
+                "omega": 0.01,
+                "phi": 0.01,
+                "kappa": 0.01,
+            },
+            max_nfev=50,
+        )
+
+        self.assertTrue(result.success, msg=result.message)
+        for cam_index in fixed_indices:
+            np.testing.assert_allclose(
+                refined_cals[cam_index].get_pos(),
+                start_cals[cam_index].get_pos(),
+                atol=1e-12,
+            )
+            np.testing.assert_allclose(
+                refined_cals[cam_index].get_angles(),
+                start_cals[cam_index].get_angles(),
+                atol=1e-12,
+            )
+        self.assertEqual(result["optimized_camera_indices"], [2, 3])
+        self.assertLess(
+            result["final_reprojection_rms"], result["initial_reprojection_rms"]
+        )
+
+    def test_known_points_require_point_optimization(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        with self.assertRaises(ValueError):
+            multi_camera_bundle_adjustment(
+                observed_pixels,
+                self.perturb_calibrations(self.true_cals),
+                self.control,
+                OrientPar(),
+                point_init=points,
+                fixed_camera_indices=[0, 1],
+                optimize_points=False,
+                known_points={0: points[0]},
+                known_point_sigmas=1e-3,
+            )
+
     def test_guarded_two_step_bundle_adjustment_rejects_bad_intrinsics_stage(self):
         points = np.array(
             [
@@ -332,6 +497,9 @@ def test_guarded_two_step_bundle_adjustment_rejects_bad_intrinsics_stage(self):
             )
 
         self.assertEqual(mocked_adjustment.call_count, 2)
+        for call in mocked_adjustment.call_args_list:
+            self.assertIsNone(call.kwargs.get("known_points"))
+            self.assertIsNone(call.kwargs.get("known_point_sigmas"))
         self.assertEqual(summary["accepted_stage"], "pose")
         self.assertLess(
             summary["pose_reprojection_rms"], summary["baseline_reprojection_rms"]
@@ -430,6 +598,269 @@ def test_guarded_two_step_bundle_adjustment_preserves_pose_when_intrinsics_are_t
         )
         self.assertEqual(final_points.shape, points.shape)
 
+    def test_guarded_two_step_bundle_adjustment_passes_known_point_constraints(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+        known_points = {0: points[0], 1: points[1]}
+
+        with patch(
+            "openptv_python.orientation.multi_camera_bundle_adjustment",
+            side_effect=[
+                (self.true_cals, points.copy(), {"success": True}),
+                (self.true_cals, points.copy(), {"success": True}),
+            ],
+        ) as mocked_adjustment:
+            guarded_two_step_bundle_adjustment(
+                observed_pixels,
+                self.lightly_perturb_calibrations(self.true_cals),
+                self.control,
+                OrientPar(),
+                intrinsics,
+                point_init=points,
+                fixed_camera_indices=[0, 1],
+                known_points=known_points,
+                known_point_sigmas=1e-3,
+            )
+
+        self.assertEqual(mocked_adjustment.call_count, 2)
+        for call in mocked_adjustment.call_args_list:
+            self.assertEqual(call.kwargs["known_points"], known_points)
+            self.assertEqual(call.kwargs["known_point_sigmas"], 1e-3)
+
+    def test_guarded_two_step_bundle_adjustment_rejects_on_hard_geometry_guard(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.zeros((len(points), 4, 2), dtype=float)
+        start_cals = self.lightly_perturb_calibrations(self.true_cals)
+        pose_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        bad_intrinsic_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        bad_intrinsic_cals[2].set_pos(
+            bad_intrinsic_cals[2].get_pos() + np.array([20.0, 0.0, 0.0])
+        )
+        bad_intrinsic_cals[2].set_angles(
+            bad_intrinsic_cals[2].get_angles() + np.array([0.0, 0.05, 0.0])
+        )
+
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+
+        with patch(
+            "openptv_python.orientation.multi_camera_bundle_adjustment",
+            side_effect=[
+                (pose_cals, points.copy(), {"success": True}),
+                (bad_intrinsic_cals, points.copy(), {"success": True}),
+            ],
+        ), patch(
+            "openptv_python.orientation.reprojection_rms",
+            side_effect=[10.0, 5.0, 4.0],
+        ), patch(
+            "openptv_python.orientation.mean_ray_convergence",
+            side_effect=[3.0, 2.0, 1.0],
+        ), patch(
+            "openptv_python.orientation.img_coord",
+            side_effect=lambda point, cal, _mm: (
+                float(point[0] + cal.get_pos()[0]),
+                float(point[1] + cal.get_pos()[1]),
+            ),
+        ), patch(
+            "openptv_python.orientation.metric_to_pixel",
+            side_effect=lambda x, y, _cpar: np.array([x, y], dtype=float),
+        ):
+            final_cals, _, summary = guarded_two_step_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                intrinsics,
+                point_init=points,
+                fixed_camera_indices=[0, 1],
+                geometry_reference_points=points,
+                geometry_reference_cals=self.true_cals,
+                geometry_guard_mode="hard",
+                geometry_guard_threshold=1.0,
+            )
+
+        self.assertEqual(summary["accepted_stage"], "pose")
+        self.assertTrue(summary["pose_geometry_ok"])
+        self.assertFalse(summary["intrinsic_geometry_ok"])
+        self.assertGreater(summary["intrinsic_geometry_max"], 1.0)
+        np.testing.assert_allclose(final_cals[2].get_pos(), pose_cals[2].get_pos())
+
+    def test_guarded_two_step_bundle_adjustment_rejects_on_soft_geometry_guard(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.zeros((len(points), 4, 2), dtype=float)
+        start_cals = self.lightly_perturb_calibrations(self.true_cals)
+        pose_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        bad_intrinsic_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        bad_intrinsic_cals[3].set_pos(
+            bad_intrinsic_cals[3].get_pos() + np.array([2.0, 0.0, 0.0])
+        )
+
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+
+        with patch(
+            "openptv_python.orientation.multi_camera_bundle_adjustment",
+            side_effect=[
+                (pose_cals, points.copy(), {"success": True}),
+                (bad_intrinsic_cals, points.copy(), {"success": True}),
+            ],
+        ), patch(
+            "openptv_python.orientation.reprojection_rms",
+            side_effect=[10.0, 5.0, 4.0],
+        ), patch(
+            "openptv_python.orientation.mean_ray_convergence",
+            side_effect=[3.0, 2.0, 1.0],
+        ), patch(
+            "openptv_python.orientation.img_coord",
+            side_effect=lambda point, cal, _mm: (
+                float(point[0] + cal.get_pos()[0]),
+                float(point[1] + cal.get_pos()[1]),
+            ),
+        ), patch(
+            "openptv_python.orientation.metric_to_pixel",
+            side_effect=lambda x, y, _cpar: np.array([x, y], dtype=float),
+        ):
+            final_cals, _, summary = guarded_two_step_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                intrinsics,
+                point_init=points,
+                fixed_camera_indices=[0, 1],
+                geometry_reference_points=points,
+                geometry_reference_cals=self.true_cals,
+                geometry_guard_mode="soft",
+            )
+
+        self.assertEqual(summary["accepted_stage"], "pose")
+        self.assertTrue(summary["pose_geometry_ok"])
+        self.assertFalse(summary["intrinsic_geometry_ok"])
+        self.assertGreater(
+            summary["intrinsic_geometry_max"],
+            summary["pose_geometry_max"],
+        )
+        np.testing.assert_allclose(final_cals[3].get_pos(), pose_cals[3].get_pos())
+
+    def test_cavity_intrinsics_only_improves_from_intrinsic_perturbation(self):
+        cavity_dir = Path("tests/testing_fodder/test_cavity")
+        control = ControlPar(4).from_file(cavity_dir / "parameters/ptv.par")
+        true_cals = [
+            read_calibration(
+                cavity_dir / f"cal/cam{cam_num}.tif.ori",
+                cavity_dir / f"cal/cam{cam_num}.tif.addpar",
+            )
+            for cam_num in range(1, 5)
+        ]
+
+        cor_buf, path_buf = read_path_frame(
+            str(cavity_dir / "res_orig/rt_is"),
+            "",
+            "",
+            10001,
+        )
+        targets = [
+            read_targets(str(cavity_dir / f"img_orig/cam{cam_num}.%05d"), 10001)
+            for cam_num in range(1, 5)
+        ]
+        subset = [
+            pt_num for pt_num, corres in enumerate(cor_buf) if np.all(corres.p >= 0)
+        ][:24]
+        observed_pixels = np.full((len(subset), 4, 2), np.nan, dtype=float)
+        point_init = np.empty((len(subset), 3), dtype=float)
+        for out_num, pt_num in enumerate(subset):
+            point_init[out_num] = path_buf[pt_num].x
+            for cam in range(4):
+                target_index = cor_buf[pt_num].p[cam]
+                observed_pixels[out_num, cam, 0] = targets[cam][target_index].x
+                observed_pixels[out_num, cam, 1] = targets[cam][target_index].y
+
+        start_cals = [self.clone_calibration(cal) for cal in true_cals]
+        for cal in start_cals:
+            cal.added_par[0] += 2e-5
+            cal.added_par[3] += 8e-5
+            cal.added_par[4] -= 6e-5
+
+        before_rms = reprojection_rms(observed_pixels, point_init, start_cals, control)
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+
+        refined_cals, refined_points, result = multi_camera_bundle_adjustment(
+            observed_pixels,
+            start_cals,
+            control,
+            intrinsics,
+            point_init=point_init.copy(),
+            fixed_camera_indices=[0, 1, 2, 3],
+            optimize_extrinsics=False,
+            optimize_points=False,
+            loss="linear",
+            method="trf",
+            prior_sigmas={
+                "k1": 5e-5,
+                "p1": 1e-4,
+                "p2": 1e-4,
+            },
+            parameter_bounds={
+                "k1": (-5e-5, 5e-5),
+                "p1": (-2e-4, 2e-4),
+                "p2": (-2e-4, 2e-4),
+            },
+            max_nfev=40,
+        )
+        after_rms = reprojection_rms(
+            observed_pixels,
+            refined_points,
+            refined_cals,
+            control,
+        )
+
+        self.assertTrue(result.success, msg=result.message)
+        self.assertLess(after_rms, before_rms - 0.1)
+        for refined, start in zip(refined_cals, start_cals):
+            np.testing.assert_allclose(refined.get_pos(), start.get_pos(), atol=1e-12)
+            np.testing.assert_allclose(refined.get_angles(), start.get_angles(), atol=1e-12)
+        np.testing.assert_allclose(refined_points, point_init)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_demo_bundle_adjustment.py b/tests/test_demo_bundle_adjustment.py
new file mode 100644
index 0000000..dcdb78e
--- /dev/null
+++ b/tests/test_demo_bundle_adjustment.py
@@ -0,0 +1,240 @@
+import unittest
+from pathlib import Path
+
+import numpy as np
+
+from openptv_python.demo_bundle_adjustment import (
+    ExperimentResult,
+    all_fixed_camera_pairs,
+    build_experiment_start_calibrations,
+    build_known_point_constraints,
+    calibration_body_projection_drift,
+    default_experiments,
+    format_quadruplet_sensitivity,
+    load_calibrations,
+    load_reference_geometry_points,
+    perturb_calibrations,
+    summarize_epipolar_consistency,
+    summarize_fixed_camera_diagnostics,
+    summarize_quadruplet_sensitivity,
+)
+from openptv_python.calibration import read_calibration
+from openptv_python.parameters import ControlPar, read_volume_par
+from openptv_python.tracking_frame_buf import read_path_frame, read_targets
+
+
+class TestBundleAdjustmentDemo(unittest.TestCase):
+    def test_build_known_point_constraints_returns_copied_subset(self):
+        point_init = np.arange(30, dtype=float).reshape(10, 3)
+
+        known_points = build_known_point_constraints(point_init, 4)
+
+        self.assertEqual(sorted(known_points), [0, 3, 6, 9])
+        np.testing.assert_allclose(known_points[6], point_init[6])
+
+        point_init[6, 0] = -999.0
+        self.assertNotEqual(known_points[6][0], point_init[6, 0])
+
+    def test_default_experiments_adds_known_point_presets(self):
+        known_points = {0: np.array([0.0, 0.0, 0.0]), 5: np.array([1.0, 2.0, 3.0])}
+
+        experiments = default_experiments(
+            known_points=known_points,
+            known_point_sigmas=0.25,
+        )
+
+        names = [spec.name for spec in experiments]
+        self.assertIn("intrinsics_only", names)
+        self.assertIn("pose_trf_known_points", names)
+        self.assertIn("guarded_two_step_known_points", names)
+
+        intrinsics_only = next(
+            spec for spec in experiments if spec.name == "intrinsics_only"
+        )
+        self.assertFalse(intrinsics_only.ba_kwargs["optimize_extrinsics"])
+        self.assertFalse(intrinsics_only.ba_kwargs["optimize_points"])
+        self.assertEqual(
+            intrinsics_only.ba_kwargs["fixed_camera_indices"],
+            [0, 1, 2, 3],
+        )
+        self.assertTrue(intrinsics_only.ba_kwargs["use_reference_cals"])
+        self.assertTrue(intrinsics_only.ba_kwargs["perturb_intrinsics_only"])
+        self.assertEqual(intrinsics_only.ba_kwargs["intrinsic_perturbation_scale"], 1.0)
+
+        pose_spec = next(
+            spec for spec in experiments if spec.name == "pose_trf_known_points"
+        )
+        guarded_spec = next(
+            spec
+            for spec in experiments
+            if spec.name == "guarded_two_step_known_points"
+        )
+        self.assertIs(pose_spec.ba_kwargs["known_points"], known_points)
+        self.assertEqual(pose_spec.ba_kwargs["known_point_sigmas"], 0.25)
+        self.assertIs(guarded_spec.ba_kwargs["known_points"], known_points)
+        self.assertEqual(guarded_spec.ba_kwargs["known_point_sigmas"], 0.25)
+        self.assertEqual(guarded_spec.ba_kwargs["geometry_guard_mode"], "off")
+        self.assertIsNone(guarded_spec.ba_kwargs["geometry_guard_threshold"])
+
+    def test_default_experiments_accepts_geometry_guard_configuration(self):
+        experiments = default_experiments(
+            perturbation_scale=1.0,
+            geometry_guard_mode="hard",
+            geometry_guard_threshold=2.5,
+        )
+
+        guarded_spec = next(
+            spec for spec in experiments if spec.name == "guarded_two_step"
+        )
+
+        self.assertEqual(guarded_spec.ba_kwargs["geometry_guard_mode"], "hard")
+        self.assertEqual(guarded_spec.ba_kwargs["geometry_guard_threshold"], 2.5)
+
+    def test_pose_demo_keeps_fixed_cameras_on_reference_geometry(self):
+        cavity_dir = Path("tests/testing_fodder/test_cavity")
+        control = ControlPar(4).from_file(cavity_dir / "parameters/ptv.par")
+        true_cals = load_calibrations(cavity_dir, 4)
+        start_cals = perturb_calibrations(true_cals, 1.0)
+        spec = next(
+            experiment
+            for experiment in default_experiments(perturbation_scale=1.0)
+            if experiment.name == "pose_trf_linear"
+        )
+
+        working_cals = build_experiment_start_calibrations(
+            spec,
+            start_cals=start_cals,
+            reference_cals=true_cals,
+        )
+        geometry_points = load_reference_geometry_points(cavity_dir, 4)
+        drift = calibration_body_projection_drift(
+            true_cals,
+            working_cals,
+            control,
+            geometry_points,
+        )
+
+        self.assertIsNotNone(drift)
+        np.testing.assert_allclose(working_cals[0].get_pos(), true_cals[0].get_pos())
+        np.testing.assert_allclose(working_cals[1].get_pos(), true_cals[1].get_pos())
+        np.testing.assert_allclose(
+            working_cals[0].get_angles(),
+            true_cals[0].get_angles(),
+        )
+        np.testing.assert_allclose(
+            working_cals[1].get_angles(),
+            true_cals[1].get_angles(),
+        )
+        self.assertLess(drift[0].max_distance, 1e-9)
+        self.assertLess(drift[1].max_distance, 1e-9)
+        self.assertGreater(drift[2].max_distance, 0.1)
+
+    def test_all_fixed_camera_pairs_returns_all_unique_pairs(self):
+        self.assertEqual(
+            all_fixed_camera_pairs(4),
+            [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)],
+        )
+
+    def test_summarize_fixed_camera_diagnostics_orders_by_fixed_drift_then_rms(self):
+        results = [
+            ExperimentResult(
+                name="run_b",
+                description="",
+                duration_sec=0.2,
+                success=True,
+                initial_rms=4.0,
+                final_rms=1.3,
+                baseline_ray_convergence=2.0,
+                final_ray_convergence=1.1,
+                notes="b",
+                cal_dir=None,
+                fixed_camera_indices=(0, 2),
+                camera_position_shifts=[0.0, 0.8, 0.0, 0.6],
+                camera_angle_shifts=[0.0, 0.02, 0.0, 0.01],
+                refined_cals=None,
+                refined_points=None,
+            ),
+            ExperimentResult(
+                name="run_a",
+                description="",
+                duration_sec=0.1,
+                success=True,
+                initial_rms=4.0,
+                final_rms=1.2,
+                baseline_ray_convergence=2.0,
+                final_ray_convergence=1.0,
+                notes="a",
+                cal_dir=None,
+                fixed_camera_indices=(0, 1),
+                camera_position_shifts=[0.0, 0.0, 0.5, 0.4],
+                camera_angle_shifts=[0.0, 0.0, 0.01, 0.02],
+                refined_cals=None,
+                refined_points=None,
+            ),
+        ]
+
+        diagnostics = summarize_fixed_camera_diagnostics(results)
+
+        self.assertEqual(diagnostics[0].fixed_camera_indices, (0, 1))
+        self.assertEqual(diagnostics[1].fixed_camera_indices, (0, 2))
+        self.assertEqual(diagnostics[0].fixed_position_shift, 0.0)
+        self.assertAlmostEqual(diagnostics[0].mean_free_position_shift, 0.45)
+
+    def test_epipolar_and_quadruplet_diagnostics_detect_perturbation(self):
+        cavity_dir = Path("tests/testing_fodder/test_cavity")
+        control = ControlPar(4).from_file(cavity_dir / "parameters/ptv.par")
+        vpar = read_volume_par(cavity_dir / "parameters/criteria.par")
+        cals = [
+            read_calibration(
+                cavity_dir / f"cal/cam{cam_num}.tif.ori",
+                cavity_dir / f"cal/cam{cam_num}.tif.addpar",
+            )
+            for cam_num in range(1, 5)
+        ]
+
+        cor_buf, path_buf = read_path_frame(str(cavity_dir / "res_orig/rt_is"), "", "", 10001)
+        targets = [
+            read_targets(str(cavity_dir / f"img_orig/cam{cam_num}.%05d"), 10001)
+            for cam_num in range(1, 5)
+        ]
+        subset = [
+            pt_num for pt_num, corres in enumerate(cor_buf) if np.all(corres.p >= 0)
+        ][:8]
+        observed_pixels = np.full((len(subset), 4, 2), np.nan, dtype=float)
+        for out_num, pt_num in enumerate(subset):
+            for cam in range(4):
+                target_index = cor_buf[pt_num].p[cam]
+                observed_pixels[out_num, cam, 0] = targets[cam][target_index].x
+                observed_pixels[out_num, cam, 1] = targets[cam][target_index].y
+
+        baseline_epipolar = summarize_epipolar_consistency(
+            observed_pixels,
+            cals,
+            control,
+            vpar,
+            num_curve_points=16,
+        )
+        baseline_quad = summarize_quadruplet_sensitivity(observed_pixels, cals, control)
+
+        perturbed = observed_pixels.copy()
+        perturbed[0, 1, 0] += 25.0
+        perturbed[0, 1, 1] -= 15.0
+        perturbed_epipolar = summarize_epipolar_consistency(
+            perturbed,
+            cals,
+            control,
+            vpar,
+            num_curve_points=16,
+        )
+        perturbed_quad = summarize_quadruplet_sensitivity(perturbed, cals, control)
+
+        self.assertGreater(
+            max(item.max_distance for item in perturbed_epipolar),
+            max(item.max_distance for item in baseline_epipolar),
+        )
+        self.assertGreater(perturbed_quad.max_spread, baseline_quad.max_spread)
+        self.assertIn("baseline", format_quadruplet_sensitivity(baseline_quad, perturbed_quad))
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/tests/test_synthetic_cavity_case.py b/tests/test_synthetic_cavity_case.py
new file mode 100644
index 0000000..f2ca0bb
--- /dev/null
+++ b/tests/test_synthetic_cavity_case.py
@@ -0,0 +1,270 @@
+from pathlib import Path
+
+import numpy as np
+
+from openptv_python.calibration import read_calibration
+from openptv_python.demo_bundle_adjustment import load_case_observations
+from openptv_python.generate_synthetic_cavity_case import (
+    DEFAULT_OUTPUT_CASE,
+    project_pixels,
+)
+from openptv_python.orientation import multi_camera_bundle_adjustment, reprojection_rms
+from openptv_python.parameters import ControlPar, OrientPar, SequencePar
+
+
+def test_synthetic_cavity_case_exists_and_is_coherent():
+    case_dir = Path(DEFAULT_OUTPUT_CASE)
+    assert case_dir.exists()
+    assert (case_dir / "ground_truth/manifest.json").exists()
+    assert (case_dir / "ground_truth/calibration_body_points.txt").exists()
+
+    truth_cals = [
+        read_calibration(
+            case_dir / f"ground_truth/cal/cam{camera_index}.tif.ori",
+            case_dir / f"ground_truth/cal/cam{camera_index}.tif.addpar",
+        )
+        for camera_index in range(1, 5)
+    ]
+    working_cals = [
+        read_calibration(
+            case_dir / f"cal/cam{camera_index}.tif.ori",
+            case_dir / f"cal/cam{camera_index}.tif.addpar",
+        )
+        for camera_index in range(1, 5)
+    ]
+
+    position_errors = [
+        np.linalg.norm(working.get_pos() - truth.get_pos())
+        for working, truth in zip(working_cals, truth_cals)
+    ]
+    angle_errors = [
+        np.linalg.norm(working.get_angles() - truth.get_angles())
+        for working, truth in zip(working_cals, truth_cals)
+    ]
+    assert max(position_errors) < 0.2
+    assert max(angle_errors) < 0.01
+
+    control, observed_pixels, point_init = load_case_observations(
+        case_dir,
+        4,
+        max_frames=1,
+        max_points_per_frame=16,
+    )
+    assert observed_pixels.shape == (16, 4, 2)
+    rms = reprojection_rms(observed_pixels, point_init, working_cals, control)
+    assert rms < 0.5
+
+
+def load_truth_particles(
+    case_dir: Path,
+    *,
+    max_frames: int | None,
+    max_points_per_frame: int | None,
+) -> np.ndarray:
+    """Load the exact synthetic particle coordinates in sequence order."""
+    seq = SequencePar.from_file(case_dir / "parameters/sequence.par", 4)
+    frames = list(range(seq.first, seq.last + 1))
+    if max_frames is not None:
+        frames = frames[:max_frames]
+
+    truth_batches = []
+    for frame in frames:
+        frame_points = np.loadtxt(
+            case_dir / "ground_truth/particles" / f"frame_{frame}.txt",
+            skiprows=1,
+        )
+        if frame_points.ndim == 1:
+            frame_points = frame_points.reshape(1, 3)
+        if max_points_per_frame is not None:
+            frame_points = frame_points[:max_points_per_frame]
+        truth_batches.append(frame_points)
+
+    return np.concatenate(truth_batches, axis=0)
+
+
+def perturb_free_cameras(truth_cals):
+    """Perturb only the cameras that remain free during BA."""
+    deltas = {
+        2: (np.array([0.9, -0.6, 0.4]), np.array([0.008, -0.006, 0.004])),
+        3: (np.array([-0.7, 0.5, -0.3]), np.array([-0.007, 0.005, -0.004])),
+    }
+    perturbed = []
+    for camera_index, cal in enumerate(truth_cals):
+        trial = cal.__class__(
+            ext_par=cal.ext_par.copy(),
+            int_par=cal.int_par.copy(),
+            glass_par=cal.glass_par.copy(),
+            added_par=cal.added_par.copy(),
+            mmlut=cal.mmlut,
+            mmlut_data=cal.mmlut_data,
+        )
+        if camera_index in deltas:
+            pos_delta, angle_delta = deltas[camera_index]
+            trial.set_pos(trial.get_pos() + pos_delta)
+            trial.set_angles(trial.get_angles() + angle_delta)
+        perturbed.append(trial)
+    return perturbed
+
+
+def perturb_intrinsics(truth_cals):
+    """Perturb a small subset of intrinsic parameters while keeping poses fixed."""
+    perturbed = []
+    for cal in truth_cals:
+        trial = cal.__class__(
+            ext_par=cal.ext_par.copy(),
+            int_par=cal.int_par.copy(),
+            glass_par=cal.glass_par.copy(),
+            added_par=cal.added_par.copy(),
+            mmlut=cal.mmlut,
+            mmlut_data=cal.mmlut_data,
+        )
+        trial.added_par[0] += 2e-5
+        trial.added_par[3] += 8e-5
+        trial.added_par[4] -= 6e-5
+        perturbed.append(trial)
+    return perturbed
+
+
+def test_synthetic_case_bundle_adjustment_recovers_ground_truth_from_controlled_perturbation():
+    case_dir = Path(DEFAULT_OUTPUT_CASE)
+    control = ControlPar(4).from_file(case_dir / "parameters/ptv.par")
+    truth_cals = [
+        read_calibration(
+            case_dir / f"ground_truth/cal/cam{camera_index}.tif.ori",
+            case_dir / f"ground_truth/cal/cam{camera_index}.tif.addpar",
+        )
+        for camera_index in range(1, 5)
+    ]
+    truth_points = load_truth_particles(
+        case_dir,
+        max_frames=1,
+        max_points_per_frame=24,
+    )
+    observed_pixels = project_pixels(truth_points, truth_cals, control)
+
+    start_cals = perturb_free_cameras(truth_cals)
+    point_init = truth_points + np.array([0.35, -0.25, 0.18])
+
+    initial_camera_errors = [
+        np.linalg.norm(start_cals[camera_index].get_pos() - truth_cals[camera_index].get_pos())
+        for camera_index in (2, 3)
+    ]
+    initial_angle_errors = [
+        np.linalg.norm(start_cals[camera_index].get_angles() - truth_cals[camera_index].get_angles())
+        for camera_index in (2, 3)
+    ]
+    initial_point_error = float(
+        np.mean(np.linalg.norm(point_init - truth_points, axis=1))
+    )
+
+    refined_cals, refined_points, result = multi_camera_bundle_adjustment(
+        observed_pixels,
+        start_cals,
+        control,
+        OrientPar(),
+        point_init=point_init,
+        fixed_camera_indices=[0, 1],
+        loss="linear",
+        method="lm",
+        prior_sigmas={
+            "x0": 1.0,
+            "y0": 1.0,
+            "z0": 1.0,
+            "omega": 0.01,
+            "phi": 0.01,
+            "kappa": 0.01,
+        },
+        max_nfev=100,
+    )
+
+    final_camera_errors = [
+        np.linalg.norm(refined_cals[camera_index].get_pos() - truth_cals[camera_index].get_pos())
+        for camera_index in (2, 3)
+    ]
+    final_angle_errors = [
+        np.linalg.norm(refined_cals[camera_index].get_angles() - truth_cals[camera_index].get_angles())
+        for camera_index in (2, 3)
+    ]
+    final_point_error = float(
+        np.mean(np.linalg.norm(refined_points - truth_points, axis=1))
+    )
+
+    assert bool(result.success), result.message
+    assert max(final_camera_errors) < max(initial_camera_errors) * 0.01
+    assert max(final_camera_errors) < 0.01
+    assert max(final_angle_errors) < max(initial_angle_errors) * 0.01
+    assert max(final_angle_errors) < 1e-4
+    assert final_point_error < initial_point_error * 1e-3
+    assert final_point_error < 1e-3
+    assert result["final_reprojection_rms"] < 1e-3
+
+
+def test_synthetic_case_intrinsics_only_recovers_ground_truth_from_controlled_perturbation():
+    case_dir = Path(DEFAULT_OUTPUT_CASE)
+    control = ControlPar(4).from_file(case_dir / "parameters/ptv.par")
+    truth_cals = [
+        read_calibration(
+            case_dir / f"ground_truth/cal/cam{camera_index}.tif.ori",
+            case_dir / f"ground_truth/cal/cam{camera_index}.tif.addpar",
+        )
+        for camera_index in range(1, 5)
+    ]
+    truth_points = load_truth_particles(
+        case_dir,
+        max_frames=1,
+        max_points_per_frame=24,
+    )
+    observed_pixels = project_pixels(truth_points, truth_cals, control)
+    start_cals = perturb_intrinsics(truth_cals)
+
+    intrinsics = OrientPar()
+    intrinsics.k1flag = 1
+    intrinsics.p1flag = 1
+    intrinsics.p2flag = 1
+
+    refined_cals, refined_points, result = multi_camera_bundle_adjustment(
+        observed_pixels,
+        start_cals,
+        control,
+        intrinsics,
+        point_init=truth_points.copy(),
+        fixed_camera_indices=[0, 1, 2, 3],
+        optimize_extrinsics=False,
+        optimize_points=False,
+        loss="linear",
+        method="trf",
+        prior_sigmas={
+            "k1": 5e-5,
+            "p1": 1e-4,
+            "p2": 1e-4,
+        },
+        parameter_bounds={
+            "k1": (-5e-5, 5e-5),
+            "p1": (-2e-4, 2e-4),
+            "p2": (-2e-4, 2e-4),
+        },
+        max_nfev=40,
+    )
+
+    k1_errors = [
+        abs(refined.added_par[0] - truth.added_par[0])
+        for refined, truth in zip(refined_cals, truth_cals)
+    ]
+    p1_errors = [
+        abs(refined.added_par[3] - truth.added_par[3])
+        for refined, truth in zip(refined_cals, truth_cals)
+    ]
+    p2_errors = [
+        abs(refined.added_par[4] - truth.added_par[4])
+        for refined, truth in zip(refined_cals, truth_cals)
+    ]
+
+    assert bool(result.success), result.message
+    assert np.max(np.linalg.norm(refined_points - truth_points, axis=1)) == 0.0
+    for refined, start in zip(refined_cals, start_cals):
+        np.testing.assert_allclose(refined.get_pos(), start.get_pos(), atol=1e-12)
+        np.testing.assert_allclose(refined.get_angles(), start.get_angles(), atol=1e-12)
+    assert max(k1_errors) < 1.5e-5
+    assert max(p1_errors) < 1.5e-5
+    assert max(p2_errors) < 3.0e-5
+    assert result["final_reprojection_rms"] < result["initial_reprojection_rms"] * 0.2
\ No newline at end of file
diff --git a/tests/testing_fodder/test_cavity_synthetic/README.md b/tests/testing_fodder/test_cavity_synthetic/README.md
new file mode 100644
index 0000000..d1babae
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/README.md
@@ -0,0 +1,14 @@
+# Synthetic Cavity Case
+
+This case is generated deterministically from the geometry of `test_cavity`, but all observations come from known ground truth.
+
+Contents:
+
+- `cal/`: working calibrations recovered from synthetic calibration-body targets using `full_calibration`.
+- `ground_truth/cal/`: exact camera models used to project the synthetic data.
+- `ground_truth/calibration_body_points.txt`: known 3D calibration-body points.
+- `calibration_targets/`: synthetic target files for that calibration body.
+- `img_orig/`: synthetic particle target files for two frames.
+- `res_orig/`: synthetic `rt_is`, `ptv_is`, and `added` files for those frames.
+- `ground_truth/particles/`: exact 3D particle coordinates per frame.
+- `ground_truth/manifest.json`: generation seed and calibration-recovery errors.
diff --git a/tests/testing_fodder/test_cavity_synthetic/cal/calblock.txt b/tests/testing_fodder/test_cavity_synthetic/cal/calblock.txt
new file mode 100644
index 0000000..87bfe1f
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/cal/calblock.txt
@@ -0,0 +1,48 @@
+          1    -36.000    -16.000    -16.000
+          2    -36.000    -16.000     -5.333
+          3    -36.000    -16.000      5.333
+          4    -36.000    -16.000     16.000
+          5    -36.000     -9.600    -16.000
+          6    -36.000     -9.600     -5.333
+          7    -36.000     -9.600      5.333
+          8    -36.000     -9.600     16.000
+          9    -36.000     -3.200    -16.000
+         10    -36.000     -3.200     -5.333
+         11    -36.000     -3.200      5.333
+         12    -36.000     -3.200     16.000
+         13    -36.000      3.200    -16.000
+         14    -36.000      3.200     -5.333
+         15    -36.000      3.200      5.333
+         16    -36.000      3.200     16.000
+         17    -36.000      9.600    -16.000
+         18    -36.000      9.600     -5.333
+         19    -36.000      9.600      5.333
+         20    -36.000      9.600     16.000
+         21    -36.000     16.000    -16.000
+         22    -36.000     16.000     -5.333
+         23    -36.000     16.000      5.333
+         24    -36.000     16.000     16.000
+         25    -25.714    -16.000    -16.000
+         26    -25.714    -16.000     -5.333
+         27    -25.714    -16.000      5.333
+         28    -25.714    -16.000     16.000
+         29    -25.714     -9.600    -16.000
+         30    -25.714     -9.600     -5.333
+         31    -25.714     -9.600      5.333
+         32    -25.714     -9.600     16.000
+         33    -25.714     -3.200    -16.000
+         34    -25.714     -3.200     -5.333
+         35    -25.714     -3.200      5.333
+         36    -25.714     -3.200     16.000
+         37    -25.714      3.200    -16.000
+         38    -25.714      3.200     -5.333
+         39    -25.714      3.200      5.333
+         40    -25.714      3.200     16.000
+         41    -25.714      9.600    -16.000
+         42    -25.714      9.600     -5.333
+         43    -25.714      9.600      5.333
+         44    -25.714      9.600     16.000
+         45    -25.714     16.000    -16.000
+         46    -25.714     16.000     -5.333
+         47    -25.714     16.000      5.333
+         48    -25.714     16.000     16.000
diff --git a/tests/testing_fodder/test_cavity_synthetic/cal/cam1.tif.addpar b/tests/testing_fodder/test_cavity_synthetic/cal/cam1.tif.addpar
new file mode 100644
index 0000000..2916c6a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/cal/cam1.tif.addpar
@@ -0,0 +1 @@
+0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 1.00000000 0.00000000
diff --git a/tests/testing_fodder/test_cavity_synthetic/cal/cam1.tif.ori b/tests/testing_fodder/test_cavity_synthetic/cal/cam1.tif.ori
new file mode 100644
index 0000000..2700f81
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/cal/cam1.tif.ori
@@ -0,0 +1,11 @@
+82.96897532 12.21372353 -569.03076947
+-56.54284096 2.97360259 56.53126707
+
+-0.9857736 -0.0171549 0.1672010
+-0.0164254 0.9998486 0.0057447
+-0.1672743 0.0029167 -0.9859061
+
+0.0000 0.0000
+70.0000
+
+0.000000000000000 0.000000000000000 -125.000000000000000
diff --git a/tests/testing_fodder/test_cavity_synthetic/cal/cam2.tif.addpar b/tests/testing_fodder/test_cavity_synthetic/cal/cam2.tif.addpar
new file mode 100644
index 0000000..2916c6a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/cal/cam2.tif.addpar
@@ -0,0 +1 @@
+0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 1.00000000 0.00000000
diff --git a/tests/testing_fodder/test_cavity_synthetic/cal/cam2.tif.ori b/tests/testing_fodder/test_cavity_synthetic/cal/cam2.tif.ori
new file mode 100644
index 0000000..85cd577
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/cal/cam2.tif.ori
@@ -0,0 +1,11 @@
+-128.26443576 26.36339680 -572.94429259
+0.03174008 -2.91375837 -0.02126352
+
+-0.9739376 -0.0207125 -0.2258683
+-0.0284175 0.9991180 0.0309147
+0.2250288 0.0365276 -0.9736672
+
+0.0000 0.0000
+70.0000
+
+0.000000000000000 0.000000000000000 -125.000000000000000
diff --git a/tests/testing_fodder/test_cavity_synthetic/cal/cam3.tif.addpar b/tests/testing_fodder/test_cavity_synthetic/cal/cam3.tif.addpar
new file mode 100644
index 0000000..2916c6a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/cal/cam3.tif.addpar
@@ -0,0 +1 @@
+0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 1.00000000 0.00000000
diff --git a/tests/testing_fodder/test_cavity_synthetic/cal/cam3.tif.ori b/tests/testing_fodder/test_cavity_synthetic/cal/cam3.tif.ori
new file mode 100644
index 0000000..bcf3c16
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/cal/cam3.tif.ori
@@ -0,0 +1,11 @@
+-110.35331896 73.30467692 584.86753878
+-0.11168165 -0.19747520 -0.02792006
+
+0.9801829 0.0273739 -0.1961942
+-0.0058853 0.9939932 0.1092836
+0.1980072 -0.1059633 0.9744562
+
+0.0000 0.0000
+70.0000
+
+0.000000000000000 0.000000000000000 125.000000000000000
diff --git a/tests/testing_fodder/test_cavity_synthetic/cal/cam4.tif.addpar b/tests/testing_fodder/test_cavity_synthetic/cal/cam4.tif.addpar
new file mode 100644
index 0000000..2916c6a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/cal/cam4.tif.addpar
@@ -0,0 +1 @@
+0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 1.00000000 0.00000000
diff --git a/tests/testing_fodder/test_cavity_synthetic/cal/cam4.tif.ori b/tests/testing_fodder/test_cavity_synthetic/cal/cam4.tif.ori
new file mode 100644
index 0000000..3a95f4a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/cal/cam4.tif.ori
@@ -0,0 +1,11 @@
+126.01664951 68.15570699 573.11474392
+-0.11944273 0.23908264 0.00944663
+
+0.9715123 -0.0091778 0.2368115
+-0.0188378 0.9930975 0.1157695
+-0.2362394 -0.1169325 0.9646335
+
+0.0000 0.0000
+70.0000
+
+0.000000000000000 0.000000000000000 125.000000000000000
diff --git a/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam1.00001_targets b/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam1.00001_targets
new file mode 100644
index 0000000..fc9d449
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam1.00001_targets
@@ -0,0 +1,49 @@
+48
+   0  871.4976  574.4239    21     5     5  5000    -1
+   1  968.7627  780.1996    21     5     5  5000    -1
+   2  838.1284  503.3898    21     5     5  5000    -1
+   3  856.3282  643.2051    21     5     5  5000    -1
+   4  841.5711  710.1669    21     5     5  5000    -1
+   5  824.8968  639.3359    21     5     5  5000    -1
+   6  965.2590  571.2681    21     5     5  5000    -1
+   7  842.7487  779.0878    21     5     5  5000    -1
+   8  931.1273  569.2818    21     5     5  5000    -1
+   9  949.1127  638.9936    21     5     5  5000    -1
+  10  980.7167  431.1059    21     5     5  5000    -1
+  11  872.6715  645.2169    21     5     5  5000    -1
+  12  951.4404  776.4492    21     5     5  5000    -1
+  13  837.0107  434.4505    21     5     5  5000    -1
+  14  966.4111  640.9148    21     5     5  5000    -1
+  15  857.5034  713.0480    21     5     5  5000    -1
+  16  946.8500  501.5247    21     5     5  5000    -1
+  17  984.1661  642.8867    21     5     5  5000    -1
+  18  963.0019  431.9533    21     5     5  5000    -1
+  19  875.0614  786.7928    21     5     5  5000    -1
+  20  985.3463  713.4687    21     5     5  5000    -1
+  21  983.0011  572.3006    21     5     5  5000    -1
+  22  840.4085  641.2454    21     5     5  5000    -1
+  23  930.0169  501.4367    21     5     5  5000    -1
+  24  981.8513  501.7078    21     5     5  5000    -1
+  25  947.9733  570.2621    21     5     5  5000    -1
+  26  934.5580  772.7941    21     5     5  5000    -1
+  27  858.6931  782.8897    21     5     5  5000    -1
+  28  950.2685  707.7218    21     5     5  5000    -1
+  29  822.6427  503.2771    21     5     5  5000    -1
+  30  932.2542  637.1213    21     5     5  5000    -1
+  31  823.7620  571.3093    21     5     5  5000    -1
+  32  986.5417  784.0488    21     5     5  5000    -1
+  33  873.8594  716.0057    21     5     5  5000    -1
+  34  839.2610  572.3206    21     5     5  5000    -1
+  35  826.0468  707.3596    21     5     5  5000    -1
+  36  870.3378  503.6243    21     5     5  5000    -1
+  37  964.1225  501.6151    21     5     5  5000    -1
+  38  933.3978  704.9578    21     5     5  5000    -1
+  39  821.5386  435.2366    21     5     5  5000    -1
+  40  928.9232  433.5833    21     5     5  5000    -1
+  41  967.5790  710.5578    21     5     5  5000    -1
+  42  869.1919  432.8156    21     5     5  5000    -1
+  43  854.0215  503.5055    21     5     5  5000    -1
+  44  945.7430  432.7788    21     5     5  5000    -1
+  45  855.1676  573.3584    21     5     5  5000    -1
+  46  827.2123  775.3831    21     5     5  5000    -1
+  47  852.8899  433.6438    21     5     5  5000    -1
diff --git a/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam2.00001_targets b/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam2.00001_targets
new file mode 100644
index 0000000..9f839c6
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam2.00001_targets
@@ -0,0 +1,49 @@
+48
+   0  948.1816  578.7394    21     5     5  5000    -1
+   1  864.4369  504.9010    21     5     5  5000    -1
+   2  868.0688  641.8695    21     5     5  5000    -1
+   3  961.9179  574.9674    21     5     5  5000    -1
+   4  953.6742  790.3353    21     5     5  5000    -1
+   5  990.0954  635.5653    21     5     5  5000    -1
+   6  993.5576  771.1674    21     5     5  5000    -1
+   7  991.8334  703.3832    21     5     5  5000    -1
+   8  986.5779  499.8175    21     5     5  5000    -1
+   9  951.8562  719.8431    21     5     5  5000    -1
+  10  944.4550  437.4601    21     5     5  5000    -1
+  11  849.6602  507.6854    21     5     5  5000    -1
+  12  958.2539  435.5523    21     5     5  5000    -1
+  13  988.3436  567.7109    21     5     5  5000    -1
+  14  946.3248  508.1229    21     5     5  5000    -1
+  15  967.3140  783.7800    21     5     5  5000    -1
+  16  971.6972  433.6937    21     5     5  5000    -1
+  17  869.8626  710.2959    21     5     5  5000    -1
+  18  856.9919  785.0576    21     5     5  5000    -1
+  19  847.7908  438.2377    21     5     5  5000    -1
+  20  832.5949  440.1710    21     5     5  5000    -1
+  21  836.3833  580.8684    21     5     5  5000    -1
+  22  838.2562  651.1507    21     5     5  5000    -1
+  23  963.7299  644.6104    21     5     5  5000    -1
+  24  884.1750  704.9572    21     5     5  5000    -1
+  25  965.5285  714.2138    21     5     5  5000    -1
+  26  877.0319  434.5177    21     5     5  5000    -1
+  27  882.4120  637.4041    21     5     5  5000    -1
+  28  984.7982  431.8824    21     5     5  5000    -1
+  29  840.1151  721.3919    21     5     5  5000    -1
+  30  973.5059  502.5146    21     5     5  5000    -1
+  31  878.8406  502.1870    21     5     5  5000    -1
+  32  978.8499  708.7289    21     5     5  5000    -1
+  33  885.9230  772.4768    21     5     5  5000    -1
+  34  871.6416  778.6871    21     5     5  5000    -1
+  35  855.1806  715.7723    21     5     5  5000    -1
+  36  866.2603  573.4054    21     5     5  5000    -1
+  37  960.0927  505.2822    21     5     5  5000    -1
+  38  880.6339  569.8149    21     5     5  5000    -1
+  39  950.0253  649.3120    21     5     5  5000    -1
+  40  851.5149  577.0887    21     5     5  5000    -1
+  41  980.6042  777.3928    21     5     5  5000    -1
+  42  853.3550  646.4502    21     5     5  5000    -1
+  43  841.9600  791.5943    21     5     5  5000    -1
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diff --git a/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam3.00001_targets b/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam3.00001_targets
new file mode 100644
index 0000000..cc7b54d
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam3.00001_targets
@@ -0,0 +1,49 @@
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diff --git a/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam4.00001_targets b/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam4.00001_targets
new file mode 100644
index 0000000..a709b5e
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/calibration_targets/cam4.00001_targets
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam1.tif.addpar b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam1.tif.addpar
new file mode 100644
index 0000000..2916c6a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam1.tif.addpar
@@ -0,0 +1 @@
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam1.tif.ori b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam1.tif.ori
new file mode 100644
index 0000000..2700f81
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam1.tif.ori
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam2.tif.addpar b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam2.tif.addpar
new file mode 100644
index 0000000..2916c6a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam2.tif.addpar
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam2.tif.ori b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam2.tif.ori
new file mode 100644
index 0000000..85cd577
--- /dev/null
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam3.tif.addpar b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam3.tif.addpar
new file mode 100644
index 0000000..2916c6a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam3.tif.addpar
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam3.tif.ori b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam3.tif.ori
new file mode 100644
index 0000000..bcf3c16
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam3.tif.ori
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam4.tif.addpar b/tests/testing_fodder/test_cavity_synthetic/ground_truth/cal/cam4.tif.addpar
new file mode 100644
index 0000000..2916c6a
--- /dev/null
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new file mode 100644
index 0000000..3a95f4a
--- /dev/null
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/calibration_body_points.txt b/tests/testing_fodder/test_cavity_synthetic/ground_truth/calibration_body_points.txt
new file mode 100644
index 0000000..87bfe1f
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/ground_truth/calibration_body_points.txt
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/manifest.json b/tests/testing_fodder/test_cavity_synthetic/ground_truth/manifest.json
new file mode 100644
index 0000000..7822255
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/ground_truth/manifest.json
@@ -0,0 +1,18 @@
+{
+  "seed": 20260306,
+  "num_calibration_points": 48,
+  "num_frames": 2,
+  "particles_per_frame": 96,
+  "calibration_position_errors": [
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+    1.608167152903132e-13,
+    3.024609420349515e-13,
+    5.895364129370234e-13
+  ],
+  "calibration_angle_errors": [
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+    5.834553579603124e-16,
+    5.215837820588841e-16,
+    1.0547703616262308e-15
+  ]
+}
diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/particles/frame_10001.txt b/tests/testing_fodder/test_cavity_synthetic/ground_truth/particles/frame_10001.txt
new file mode 100644
index 0000000..ae9c38f
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/ground_truth/particles/frame_10001.txt
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diff --git a/tests/testing_fodder/test_cavity_synthetic/ground_truth/particles/frame_10002.txt b/tests/testing_fodder/test_cavity_synthetic/ground_truth/particles/frame_10002.txt
new file mode 100644
index 0000000..83be55f
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/ground_truth/particles/frame_10002.txt
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diff --git a/tests/testing_fodder/test_cavity_synthetic/img_orig/cam1.10001_targets b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam1.10001_targets
new file mode 100644
index 0000000..354bcbc
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam1.10001_targets
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diff --git a/tests/testing_fodder/test_cavity_synthetic/img_orig/cam1.10002_targets b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam1.10002_targets
new file mode 100644
index 0000000..cc77005
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam1.10002_targets
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diff --git a/tests/testing_fodder/test_cavity_synthetic/img_orig/cam2.10001_targets b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam2.10001_targets
new file mode 100644
index 0000000..30a4236
--- /dev/null
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diff --git a/tests/testing_fodder/test_cavity_synthetic/img_orig/cam2.10002_targets b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam2.10002_targets
new file mode 100644
index 0000000..da90835
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam2.10002_targets
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diff --git a/tests/testing_fodder/test_cavity_synthetic/img_orig/cam3.10001_targets b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam3.10001_targets
new file mode 100644
index 0000000..0b89808
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam3.10001_targets
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diff --git a/tests/testing_fodder/test_cavity_synthetic/img_orig/cam3.10002_targets b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam3.10002_targets
new file mode 100644
index 0000000..525f173
--- /dev/null
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diff --git a/tests/testing_fodder/test_cavity_synthetic/img_orig/cam4.10001_targets b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam4.10001_targets
new file mode 100644
index 0000000..7dcfafc
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam4.10001_targets
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diff --git a/tests/testing_fodder/test_cavity_synthetic/img_orig/cam4.10002_targets b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam4.10002_targets
new file mode 100644
index 0000000..6035436
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/img_orig/cam4.10002_targets
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diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/cal_ori.par b/tests/testing_fodder/test_cavity_synthetic/parameters/cal_ori.par
new file mode 100644
index 0000000..9dece9c
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/cal_ori.par
@@ -0,0 +1,12 @@
+cal/target_on_a_side.txt
+cal/cam1.tif
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diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/criteria.par b/tests/testing_fodder/test_cavity_synthetic/parameters/criteria.par
new file mode 100644
index 0000000..67cc8fa
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/criteria.par
@@ -0,0 +1,12 @@
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diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/detect_plate.par b/tests/testing_fodder/test_cavity_synthetic/parameters/detect_plate.par
new file mode 100644
index 0000000..1c67d03
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/detect_plate.par
@@ -0,0 +1,13 @@
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diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/dumbbell.par b/tests/testing_fodder/test_cavity_synthetic/parameters/dumbbell.par
new file mode 100644
index 0000000..ea931f3
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/dumbbell.par
@@ -0,0 +1,6 @@
+3.000000
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diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/examine.par b/tests/testing_fodder/test_cavity_synthetic/parameters/examine.par
new file mode 100644
index 0000000..aa47d0d
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/examine.par
@@ -0,0 +1,2 @@
+0
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diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/man_ori.par b/tests/testing_fodder/test_cavity_synthetic/parameters/man_ori.par
new file mode 100644
index 0000000..45acff6
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/man_ori.par
@@ -0,0 +1,16 @@
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diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/multi_planes.par b/tests/testing_fodder/test_cavity_synthetic/parameters/multi_planes.par
new file mode 100644
index 0000000..9b30724
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/multi_planes.par
@@ -0,0 +1,4 @@
+3
+img/calib_a_cam
+img/calib_b_cam
+img/calib_c_cam
diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/orient.par b/tests/testing_fodder/test_cavity_synthetic/parameters/orient.par
new file mode 100644
index 0000000..66f4ca4
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/orient.par
@@ -0,0 +1,12 @@
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diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/pft_version b/tests/testing_fodder/test_cavity_synthetic/parameters/pft_version
new file mode 100644
index 0000000..00750ed
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/pft_version
@@ -0,0 +1 @@
+3
diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/pft_version.par b/tests/testing_fodder/test_cavity_synthetic/parameters/pft_version.par
new file mode 100644
index 0000000..573541a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/pft_version.par
@@ -0,0 +1 @@
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diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/ptv.par b/tests/testing_fodder/test_cavity_synthetic/parameters/ptv.par
new file mode 100644
index 0000000..49c0f6f
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/ptv.par
@@ -0,0 +1,21 @@
+4
+img/cam1.10003
+cal/cam1.tif
+img/cam2.10003
+cal/cam2.tif
+img/cam3.10003
+cal/cam3.tif
+img/cam4.10003
+cal/cam4.tif
+1
+0
+1
+1280
+1024
+0.012
+0.012
+0
+1
+1.33
+1.46
+6
diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/sequence.par b/tests/testing_fodder/test_cavity_synthetic/parameters/sequence.par
new file mode 100644
index 0000000..9d9b85c
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/sequence.par
@@ -0,0 +1,6 @@
+img_orig/cam1.%05d
+img_orig/cam2.%05d
+img_orig/cam3.%05d
+img_orig/cam4.%05d
+10001
+10002
diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/shaking.par b/tests/testing_fodder/test_cavity_synthetic/parameters/shaking.par
new file mode 100644
index 0000000..06d8de2
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/shaking.par
@@ -0,0 +1,4 @@
+10000
+10004
+10
+5
diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/sortgrid.par b/tests/testing_fodder/test_cavity_synthetic/parameters/sortgrid.par
new file mode 100644
index 0000000..209e3ef
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/sortgrid.par
@@ -0,0 +1 @@
+20
diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/targ_rec.par b/tests/testing_fodder/test_cavity_synthetic/parameters/targ_rec.par
new file mode 100644
index 0000000..7800c5a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/targ_rec.par
@@ -0,0 +1,13 @@
+9
+9
+9
+11
+100
+4
+500
+2
+100
+2
+100
+150
+2
diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/track.par b/tests/testing_fodder/test_cavity_synthetic/parameters/track.par
new file mode 100644
index 0000000..31706ba
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/track.par
@@ -0,0 +1,9 @@
+-5.5
+5.5
+-5.5
+5.5
+-5.5
+5.5
+110
+1.0
+0
diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters/unsharp_mask.par b/tests/testing_fodder/test_cavity_synthetic/parameters/unsharp_mask.par
new file mode 100644
index 0000000..48082f7
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters/unsharp_mask.par
@@ -0,0 +1 @@
+12
diff --git a/tests/testing_fodder/test_cavity_synthetic/parameters_Run1.yaml b/tests/testing_fodder/test_cavity_synthetic/parameters_Run1.yaml
new file mode 100644
index 0000000..9c58258
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/parameters_Run1.yaml
@@ -0,0 +1,174 @@
+num_cams: 4
+plugins:
+  available_tracking:
+  - default
+  available_sequence:
+  - default
+  selected_tracking: default
+  selected_sequence: default
+cal_ori:
+  chfield: 0
+  fixp_name: cal/target_on_a_side.txt
+  img_cal_name:
+  - cal/cam1.tif
+  - cal/cam2.tif
+  - cal/cam3.tif
+  - cal/cam4.tif
+  img_ori:
+  - cal/cam1.tif.ori
+  - cal/cam2.tif.ori
+  - cal/cam3.tif.ori
+  - cal/cam4.tif.ori
+  pair_flag: true
+  tiff_flag: true
+  cal_splitter: false
+criteria:
+  X_lay:
+  - -40
+  - 40
+  Zmax_lay:
+  - 20
+  - 20
+  Zmin_lay:
+  - -20
+  - -20
+  cn: 0.02
+  cnx: 0.02
+  cny: 0.02
+  corrmin: 33.0
+  csumg: 0.02
+  eps0: 0.2
+detect_plate:
+  gvth_1: 40
+  gvth_2: 40
+  gvth_3: 40
+  gvth_4: 40
+  max_npix: 400
+  max_npix_x: 50
+  max_npix_y: 50
+  min_npix: 25
+  min_npix_x: 5
+  min_npix_y: 5
+  size_cross: 3
+  sum_grey: 100
+  tol_dis: 500
+dumbbell:
+  dumbbell_eps: 3.0
+  dumbbell_gradient_descent: 0.05
+  dumbbell_niter: 500
+  dumbbell_penalty_weight: 1.0
+  dumbbell_scale: 25.0
+  dumbbell_step: 1
+examine:
+  Combine_Flag: false
+  Examine_Flag: false
+man_ori:
+  nr:
+  - 3
+  - 5
+  - 72
+  - 73
+  - 3
+  - 5
+  - 72
+  - 73
+  - 1
+  - 5
+  - 71
+  - 73
+  - 1
+  - 5
+  - 71
+  - 73
+multi_planes:
+  n_planes: 3
+  plane_name:
+  - img/calib_a_cam
+  - img/calib_b_cam
+  - img/calib_c_cam
+orient:
+  cc: 0
+  interf: 0
+  k1: 0
+  k2: 0
+  k3: 0
+  p1: 0
+  p2: 0
+  pnfo: 0
+  scale: 0
+  shear: 0
+  xh: 0
+  yh: 0
+pft_version:
+  Existing_Target: 0
+ptv:
+  allcam_flag: false
+  chfield: 0
+  hp_flag: true
+  img_cal:
+  - cal/cam1.tif
+  - cal/cam2.tif
+  - cal/cam3.tif
+  - cal/cam4.tif
+  img_name:
+  - img/cam1.10003
+  - img/cam2.10003
+  - img/cam3.10003
+  - img/cam4.10003
+  imx: 1280
+  imy: 1024
+  mmp_d: 6.0
+  mmp_n1: 1.0
+  mmp_n2: 1.33
+  mmp_n3: 1.46
+  pix_x: 0.012
+  pix_y: 0.012
+  tiff_flag: true
+  splitter: false
+sequence:
+  base_name:
+  - img/cam1.%05d
+  - img/cam2.%05d
+  - img/cam3.%05d
+  - img/cam4.%05d
+  first: 10001
+  last: 10004
+shaking:
+  shaking_first_frame: 10000
+  shaking_last_frame: 10004
+  shaking_max_num_frames: 5
+  shaking_max_num_points: 10
+sortgrid:
+  radius: 20
+targ_rec:
+  cr_sz: 2
+  disco: 100
+  gvthres:
+  - 9
+  - 9
+  - 9
+  - 11
+  nnmax: 500
+  nnmin: 4
+  nxmax: 100
+  nxmin: 2
+  nymax: 100
+  nymin: 2
+  sumg_min: 150
+track:
+  angle: 110.0
+  dacc: 1.0
+  dvxmax: 5.5
+  dvxmin: -5.5
+  dvymax: 5.5
+  dvymin: -5.5
+  dvzmax: 5.5
+  dvzmin: -5.5
+  flagNewParticles: false
+masking:
+  mask_flag: false
+  mask_base_name: ''
+unsharp_mask:
+  flag: false
+  size: 3
+  strength: 1.0
diff --git a/tests/testing_fodder/test_cavity_synthetic/res_orig/added.10001 b/tests/testing_fodder/test_cavity_synthetic/res_orig/added.10001
new file mode 100644
index 0000000..61f6b2c
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/res_orig/added.10001
@@ -0,0 +1,97 @@
+96
+-1 -2 -9.015 2.927 -16.720 2
+-1 -2 -7.645 -9.020 -6.160 2
+-1 -2 19.352 -17.386 6.728 2
+-1 -2 -32.744 -5.758 14.272 2
+-1 -2 18.431 10.832 -10.569 2
+-1 -2 36.949 -5.349 -1.172 2
+-1 -2 -19.617 -4.575 4.417 2
+-1 -2 8.582 -1.118 -11.440 2
+-1 -2 30.311 4.564 -10.305 2
+-1 -2 12.792 -2.158 -16.020 2
+-1 -2 11.807 -14.954 3.007 2
+-1 -2 -7.646 -6.150 0.638 2
+-1 -2 -19.070 3.150 7.961 2
+-1 -2 28.940 11.433 -1.501 2
+-1 -2 10.934 10.566 1.142 2
+-1 -2 -0.768 5.709 -4.230 2
+-1 -2 15.329 2.295 -5.081 2
+-1 -2 7.532 0.326 -12.395 2
+-1 -2 10.426 -14.451 12.228 2
+-1 -2 32.607 -7.990 4.928 2
+-1 -2 -27.516 8.319 15.666 2
+-1 -2 -7.156 -15.113 -3.747 2
+-1 -2 -31.370 7.721 -17.849 2
+-1 -2 22.606 -8.820 12.624 2
+-1 -2 36.226 -4.620 -8.021 2
+-1 -2 -18.723 2.422 6.242 2
+-1 -2 36.212 -12.161 15.533 2
+-1 -2 -17.260 -4.648 10.483 2
+-1 -2 6.424 -0.957 16.235 2
+-1 -2 -11.474 -0.256 -1.879 2
+-1 -2 12.559 1.021 1.212 2
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+-1 -2 -31.630 -0.820 5.041 2
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+-1 -2 -34.498 8.116 -16.657 2
+-1 -2 33.681 4.878 6.307 2
+-1 -2 -28.009 -2.306 10.164 2
+-1 -2 3.044 -7.118 -7.259 2
+-1 -2 -37.934 -1.535 1.828 2
+-1 -2 4.302 10.272 4.930 2
+-1 -2 -0.895 16.169 12.916 2
+-1 -2 13.444 17.195 13.852 2
+-1 -2 13.841 13.308 10.860 2
+-1 -2 34.572 -8.234 12.524 2
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+-1 -2 20.289 2.773 17.085 2
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+-1 -2 -32.606 14.493 -0.933 2
+-1 -2 -18.860 -1.441 -8.808 2
+-1 -2 24.961 6.651 16.351 2
+-1 -2 2.688 -17.609 -10.999 2
+-1 -2 -3.655 15.607 -2.903 2
+-1 -2 15.982 9.643 -12.345 2
+-1 -2 -10.752 -6.333 17.852 2
+-1 -2 -31.511 15.417 -6.714 2
+-1 -2 -26.751 -15.001 2.024 2
+-1 -2 13.083 -13.194 10.341 2
+-1 -2 17.056 17.982 7.902 2
+-1 -2 -6.340 -10.603 -8.938 2
+-1 -2 18.118 6.123 -4.304 2
+-1 -2 -21.811 9.253 -10.665 2
+-1 -2 -37.246 16.707 -6.957 2
+-1 -2 22.396 -6.297 -3.288 2
+-1 -2 15.233 16.496 -0.485 2
+-1 -2 -36.242 -0.055 9.997 2
+-1 -2 -0.222 16.488 -11.317 2
+-1 -2 -7.595 -13.632 1.601 2
+-1 -2 6.135 -2.063 -11.965 2
+-1 -2 -8.137 1.294 1.428 2
+-1 -2 33.390 -4.392 -13.338 2
+-1 -2 -33.770 -6.056 -1.755 2
+-1 -2 10.093 12.128 8.628 2
+-1 -2 -2.671 13.008 7.810 2
+-1 -2 -27.554 -15.977 -5.876 2
+-1 -2 20.226 11.765 16.945 2
+-1 -2 -36.900 2.302 -8.408 2
+-1 -2 19.409 11.193 3.726 2
+-1 -2 20.649 4.804 15.698 2
+-1 -2 3.480 8.709 16.416 2
+-1 -2 -5.562 -9.971 -10.975 2
+-1 -2 2.171 -7.285 -2.609 2
+-1 -2 22.975 -5.970 -5.607 2
+-1 -2 -5.377 9.600 -14.614 2
+-1 -2 -12.528 13.790 17.585 2
+-1 -2 18.812 -5.990 12.050 2
+-1 -2 25.926 -9.208 6.305 2
+-1 -2 -30.580 8.931 -5.441 2
+-1 -2 -20.187 -7.884 12.874 2
diff --git a/tests/testing_fodder/test_cavity_synthetic/res_orig/added.10002 b/tests/testing_fodder/test_cavity_synthetic/res_orig/added.10002
new file mode 100644
index 0000000..a6d62af
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/res_orig/added.10002
@@ -0,0 +1,97 @@
+96
+-1 -2 33.100 -10.874 -3.731 2
+-1 -2 -1.282 -5.544 17.803 2
+-1 -2 -25.620 -7.420 -8.545 2
+-1 -2 -32.080 9.467 13.174 2
+-1 -2 21.834 7.900 10.078 2
+-1 -2 21.174 8.109 9.754 2
+-1 -2 31.382 4.365 3.668 2
+-1 -2 -22.386 -14.708 -11.052 2
+-1 -2 -23.193 4.761 2.856 2
+-1 -2 13.888 5.177 -12.495 2
+-1 -2 3.080 8.242 2.359 2
+-1 -2 -17.690 -11.043 -4.786 2
+-1 -2 -4.770 6.324 6.119 2
+-1 -2 34.353 7.246 15.820 2
+-1 -2 16.035 -2.154 -8.124 2
+-1 -2 37.414 4.711 -4.991 2
+-1 -2 32.666 -3.420 11.879 2
+-1 -2 -9.519 -0.495 -16.794 2
+-1 -2 -36.751 -12.133 16.401 2
+-1 -2 14.459 7.267 14.869 2
+-1 -2 30.360 -16.077 -17.149 2
+-1 -2 16.930 15.001 4.989 2
+-1 -2 -17.861 11.878 -2.326 2
+-1 -2 -32.018 2.278 10.487 2
+-1 -2 -25.811 -7.346 2.617 2
+-1 -2 1.807 9.784 -2.707 2
+-1 -2 -23.763 -0.427 5.064 2
+-1 -2 -18.965 12.662 17.085 2
+-1 -2 -37.281 -14.578 -16.865 2
+-1 -2 -6.163 14.971 10.397 2
+-1 -2 34.834 -11.174 -16.321 2
+-1 -2 -23.527 -16.023 0.975 2
+-1 -2 31.479 -10.415 16.823 2
+-1 -2 -10.904 17.047 -10.996 2
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+-1 -2 19.542 4.372 5.107 2
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+-1 -2 26.936 6.253 -14.169 2
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+-1 -2 -14.723 -9.350 3.145 2
+-1 -2 -19.024 -16.906 4.685 2
+-1 -2 12.761 11.736 -17.431 2
+-1 -2 22.932 -3.406 -17.093 2
+-1 -2 34.301 14.013 5.332 2
+-1 -2 24.846 -16.626 -8.352 2
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+-1 -2 -11.952 -3.570 -1.630 2
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+-1 -2 33.547 13.799 -1.132 2
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+-1 -2 33.910 -6.317 -9.369 2
+-1 -2 37.664 -4.931 -12.510 2
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+-1 -2 24.986 -9.769 -14.015 2
+-1 -2 12.407 -5.281 6.950 2
+-1 -2 -36.110 12.047 -6.674 2
+-1 -2 25.488 -15.206 -14.025 2
+-1 -2 -32.643 8.787 0.154 2
+-1 -2 -14.534 16.274 -7.342 2
+-1 -2 26.704 -17.204 5.115 2
+-1 -2 -28.329 -16.516 -15.822 2
+-1 -2 13.571 -16.186 13.031 2
+-1 -2 -11.429 -2.984 16.068 2
+-1 -2 -35.700 5.003 -7.533 2
+-1 -2 30.834 -10.881 -7.255 2
+-1 -2 27.009 5.267 12.371 2
+-1 -2 26.504 9.561 9.008 2
+-1 -2 5.367 -1.449 2.623 2
+-1 -2 -29.551 15.440 -9.108 2
+-1 -2 -18.286 2.204 -16.040 2
diff --git a/tests/testing_fodder/test_cavity_synthetic/res_orig/ptv_is.10001 b/tests/testing_fodder/test_cavity_synthetic/res_orig/ptv_is.10001
new file mode 100644
index 0000000..bb18c55
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/res_orig/ptv_is.10001
@@ -0,0 +1,97 @@
+96
+-1 -2 -9.015 2.927 -16.720
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+-1 -2 19.352 -17.386 6.728
+-1 -2 -32.744 -5.758 14.272
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+-1 -2 2.171 -7.285 -2.609
+-1 -2 22.975 -5.970 -5.607
+-1 -2 -5.377 9.600 -14.614
+-1 -2 -12.528 13.790 17.585
+-1 -2 18.812 -5.990 12.050
+-1 -2 25.926 -9.208 6.305
+-1 -2 -30.580 8.931 -5.441
+-1 -2 -20.187 -7.884 12.874
diff --git a/tests/testing_fodder/test_cavity_synthetic/res_orig/ptv_is.10002 b/tests/testing_fodder/test_cavity_synthetic/res_orig/ptv_is.10002
new file mode 100644
index 0000000..ce22d4a
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/res_orig/ptv_is.10002
@@ -0,0 +1,97 @@
+96
+-1 -2 33.100 -10.874 -3.731
+-1 -2 -1.282 -5.544 17.803
+-1 -2 -25.620 -7.420 -8.545
+-1 -2 -32.080 9.467 13.174
+-1 -2 21.834 7.900 10.078
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+-1 -2 13.888 5.177 -12.495
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+-1 -2 32.666 -3.420 11.879
+-1 -2 -9.519 -0.495 -16.794
+-1 -2 -36.751 -12.133 16.401
+-1 -2 14.459 7.267 14.869
+-1 -2 30.360 -16.077 -17.149
+-1 -2 16.930 15.001 4.989
+-1 -2 -17.861 11.878 -2.326
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+-1 -2 -23.527 -16.023 0.975
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+-1 -2 2.338 -15.091 8.570
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+-1 -2 33.547 13.799 -1.132
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+-1 -2 7.368 -6.988 14.633
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+-1 -2 37.664 -4.931 -12.510
+-1 -2 -23.598 -8.924 3.462
+-1 -2 24.986 -9.769 -14.015
+-1 -2 12.407 -5.281 6.950
+-1 -2 -36.110 12.047 -6.674
+-1 -2 25.488 -15.206 -14.025
+-1 -2 -32.643 8.787 0.154
+-1 -2 -14.534 16.274 -7.342
+-1 -2 26.704 -17.204 5.115
+-1 -2 -28.329 -16.516 -15.822
+-1 -2 13.571 -16.186 13.031
+-1 -2 -11.429 -2.984 16.068
+-1 -2 -35.700 5.003 -7.533
+-1 -2 30.834 -10.881 -7.255
+-1 -2 27.009 5.267 12.371
+-1 -2 26.504 9.561 9.008
+-1 -2 5.367 -1.449 2.623
+-1 -2 -29.551 15.440 -9.108
+-1 -2 -18.286 2.204 -16.040
diff --git a/tests/testing_fodder/test_cavity_synthetic/res_orig/rt_is.10001 b/tests/testing_fodder/test_cavity_synthetic/res_orig/rt_is.10001
new file mode 100644
index 0000000..c8f93e6
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/res_orig/rt_is.10001
@@ -0,0 +1,97 @@
+96
+1 -9.015 2.927 -16.720 18 9 7 67
+2 -7.645 -9.020 -6.160 88 83 33 52
+3 19.352 -17.386 6.728 43 31 60 63
+4 -32.744 -5.758 14.272 92 34 80 21
+5 18.431 10.832 -10.569 84 33 12 39
+6 36.949 -5.349 -1.172 16 46 27 93
+7 -19.617 -4.575 4.417 86 93 16 1
+8 8.582 -1.118 -11.440 94 7 54 81
+9 30.311 4.564 -10.305 3 24 43 16
+10 12.792 -2.158 -16.020 40 71 76 7
+11 11.807 -14.954 3.007 74 86 24 89
+12 -7.646 -6.150 0.638 26 72 56 23
+13 -19.070 3.150 7.961 89 51 30 72
+14 28.940 11.433 -1.501 72 90 61 86
+15 10.934 10.566 1.142 64 19 31 14
+16 -0.768 5.709 -4.230 56 22 68 37
+17 15.329 2.295 -5.081 5 15 50 24
+18 7.532 0.326 -12.395 85 35 23 25
+19 10.426 -14.451 12.228 37 38 89 11
+20 32.607 -7.990 4.928 58 64 63 80
+21 -27.516 8.319 15.666 63 84 13 6
+22 -7.156 -15.113 -3.747 78 82 75 53
+23 -31.370 7.721 -17.849 39 12 28 56
+24 22.606 -8.820 12.624 59 37 95 88
+25 36.226 -4.620 -8.021 14 23 70 78
+26 -18.723 2.422 6.242 60 2 74 19
+27 36.212 -12.161 15.533 80 69 77 83
+28 -17.260 -4.648 10.483 36 3 8 26
+29 6.424 -0.957 16.235 50 45 87 66
+30 -11.474 -0.256 -1.879 9 41 2 8
+31 12.559 1.021 1.212 15 14 22 76
+32 -28.189 17.607 17.740 31 66 37 84
+33 18.265 8.257 -16.566 53 44 82 41
+34 -31.630 -0.820 5.041 66 49 15 59
+35 6.814 -0.387 -14.351 17 80 64 10
+36 -35.523 -12.285 -13.003 34 25 40 69
+37 -29.445 -4.622 -4.994 57 70 57 42
+38 -29.126 -17.089 17.515 13 95 79 27
+39 32.183 -2.534 4.235 46 75 9 68
+40 -0.283 13.600 -14.159 25 61 90 85
+41 13.389 -8.503 12.506 51 13 86 38
+42 -34.498 8.116 -16.657 29 32 53 57
+43 33.681 4.878 6.307 81 50 93 65
+44 -28.009 -2.306 10.164 77 65 58 9
+45 3.044 -7.118 -7.259 11 79 59 95
+46 -37.934 -1.535 1.828 68 6 62 22
+47 4.302 10.272 4.930 6 16 94 55
+48 -0.895 16.169 12.916 32 94 3 75
+49 13.444 17.195 13.852 69 26 46 43
+50 13.841 13.308 10.860 30 1 4 32
+51 34.572 -8.234 12.524 55 62 47 92
+52 -15.387 -8.779 12.254 95 87 5 51
+53 20.289 2.773 17.085 82 43 91 13
+54 -3.234 -2.076 -13.225 91 68 0 18
+55 21.468 -15.696 2.713 47 59 25 94
+56 -32.606 14.493 -0.933 67 4 19 29
+57 -18.860 -1.441 -8.808 49 57 72 45
+58 24.961 6.651 16.351 22 67 67 70
+59 2.688 -17.609 -10.999 71 54 29 50
+60 -3.655 15.607 -2.903 2 0 49 48
+61 15.982 9.643 -12.345 65 11 18 40
+62 -10.752 -6.333 17.852 79 91 36 58
+63 -31.511 15.417 -6.714 48 60 6 0
+64 -26.751 -15.001 2.024 42 5 41 62
+65 13.083 -13.194 10.341 10 20 48 12
+66 17.056 17.982 7.902 0 92 66 60
+67 -6.340 -10.603 -8.938 23 21 26 71
+68 18.118 6.123 -4.304 76 85 83 74
+69 -21.811 9.253 -10.665 28 88 10 61
+70 -37.246 16.707 -6.957 4 78 84 91
+71 22.396 -6.297 -3.288 45 89 38 73
+72 15.233 16.496 -0.485 38 56 85 46
+73 -36.242 -0.055 9.997 75 48 17 77
+74 -0.222 16.488 -11.317 93 18 52 47
+75 -7.595 -13.632 1.601 61 55 69 79
+76 6.135 -2.063 -11.965 20 58 35 64
+77 -8.137 1.294 1.428 19 63 51 2
+78 33.390 -4.392 -13.338 41 30 39 30
+79 -33.770 -6.056 -1.755 24 77 73 31
+80 10.093 12.128 8.628 8 47 11 90
+81 -2.671 13.008 7.810 44 42 1 87
+82 -27.554 -15.977 -5.876 33 29 81 28
+83 20.226 11.765 16.945 90 40 45 82
+84 -36.900 2.302 -8.408 73 53 55 35
+85 19.409 11.193 3.726 70 76 21 44
+86 20.649 4.804 15.698 62 81 88 33
+87 3.480 8.709 16.416 27 17 32 49
+88 -5.562 -9.971 -10.975 35 36 78 17
+89 2.171 -7.285 -2.609 54 73 34 5
+90 22.975 -5.970 -5.607 52 74 92 54
+91 -5.377 9.600 -14.614 1 8 42 20
+92 -12.528 13.790 17.585 7 39 14 34
+93 18.812 -5.990 12.050 83 10 65 4
+94 25.926 -9.208 6.305 87 28 44 15
+95 -30.580 8.931 -5.441 21 52 20 36
+96 -20.187 -7.884 12.874 12 27 71 3
diff --git a/tests/testing_fodder/test_cavity_synthetic/res_orig/rt_is.10002 b/tests/testing_fodder/test_cavity_synthetic/res_orig/rt_is.10002
new file mode 100644
index 0000000..26064a4
--- /dev/null
+++ b/tests/testing_fodder/test_cavity_synthetic/res_orig/rt_is.10002
@@ -0,0 +1,97 @@
+96
+1 33.100 -10.874 -3.731 23 15 84 3
+2 -1.282 -5.544 17.803 18 8 58 19
+3 -25.620 -7.420 -8.545 0 92 32 71
+4 -32.080 9.467 13.174 85 72 37 21
+5 21.834 7.900 10.078 72 44 95 34
+6 21.174 8.109 9.754 10 85 91 1
+7 31.382 4.365 3.668 17 30 71 69
+8 -22.386 -14.708 -11.052 35 12 4 52
+9 -23.193 4.761 2.856 82 4 3 86
+10 13.888 5.177 -12.495 69 57 70 88
+11 3.080 8.242 2.359 20 34 25 90
+12 -17.690 -11.043 -4.786 60 83 77 80
+13 -4.770 6.324 6.119 29 32 38 95
+14 34.353 7.246 15.820 8 53 55 2
+15 16.035 -2.154 -8.124 46 41 15 59
+16 37.414 4.711 -4.991 84 66 28 65
+17 32.666 -3.420 11.879 39 80 92 42
+18 -9.519 -0.495 -16.794 73 63 0 22
+19 -36.751 -12.133 16.401 56 18 53 89
+20 14.459 7.267 14.869 40 47 62 23
+21 30.360 -16.077 -17.149 32 45 79 15
+22 16.930 15.001 4.989 14 75 40 27
+23 -17.861 11.878 -2.326 49 0 26 14
+24 -32.018 2.278 10.487 47 37 41 0
+25 -25.811 -7.346 2.617 15 19 75 51
+26 1.807 9.784 -2.707 30 22 72 38
+27 -23.763 -0.427 5.064 92 1 2 50
+28 -18.965 12.662 17.085 41 52 49 82
+29 -37.281 -14.578 -16.865 7 59 1 87
+30 -6.163 14.971 10.397 13 14 14 67
+31 34.834 -11.174 -16.321 38 33 9 93
+32 -23.527 -16.023 0.975 34 5 24 10
+33 31.479 -10.415 16.823 19 9 33 37
+34 -10.904 17.047 -10.996 21 3 67 47
+35 2.338 -15.091 8.570 63 79 63 8
+36 -24.309 3.802 1.276 4 81 60 9
+37 7.183 -3.599 15.415 12 31 29 55
+38 -6.721 11.602 -15.325 37 69 11 66
+39 -10.654 0.968 1.899 65 87 21 31
+40 19.542 4.372 5.107 79 55 6 83
+41 11.825 11.680 6.344 70 43 13 62
+42 -14.056 -1.276 10.696 80 62 80 70
+43 26.936 6.253 -14.169 68 90 12 73
+44 -29.039 15.648 8.516 93 40 69 28
+45 4.990 11.953 -15.933 27 11 43 46
+46 -14.723 -9.350 3.145 61 51 90 33
+47 -19.024 -16.906 4.685 26 35 46 68
+48 12.761 11.736 -17.431 53 49 66 75
+49 22.932 -3.406 -17.093 28 10 7 76
+50 34.301 14.013 5.332 95 65 54 7
+51 24.846 -16.626 -8.352 71 95 31 54
+52 29.010 7.454 -12.738 74 50 20 20
+53 -7.365 -7.525 5.655 2 91 93 61
+54 -20.517 -0.265 -12.633 89 29 42 36
+55 -14.358 3.194 13.554 36 42 35 39
+56 -15.743 -15.729 -2.462 91 20 94 17
+57 -26.586 13.217 -3.685 87 16 78 58
+58 -10.498 15.683 -6.299 44 25 86 72
+59 -15.002 8.755 1.199 11 58 22 85
+60 26.292 -1.299 1.988 76 82 34 25
+61 -11.952 -3.570 -1.630 52 27 87 35
+62 23.324 2.842 -11.248 9 56 73 11
+63 14.251 -15.957 14.159 48 74 39 30
+64 33.547 13.799 -1.132 83 89 27 41
+65 8.409 -14.003 -0.366 88 68 10 12
+66 7.368 -6.988 14.633 51 61 44 74
+67 22.414 -2.333 17.837 66 17 82 45
+68 26.398 -3.567 5.101 90 39 56 24
+69 -3.495 -2.383 6.971 42 54 61 32
+70 -4.115 9.992 5.818 86 7 8 13
+71 -1.304 3.194 -14.999 31 71 89 5
+72 14.499 9.047 -14.126 25 28 16 64
+73 -24.969 2.440 7.757 81 76 83 44
+74 -24.215 13.318 13.233 77 84 36 48
+75 3.281 -7.571 14.055 55 70 18 29
+76 -16.631 -1.079 -11.642 6 73 85 84
+77 33.910 -6.317 -9.369 24 93 45 92
+78 37.664 -4.931 -12.510 64 48 74 91
+79 -23.598 -8.924 3.462 45 46 64 78
+80 24.986 -9.769 -14.015 3 67 48 56
+81 12.407 -5.281 6.950 33 88 68 43
+82 -36.110 12.047 -6.674 75 64 30 79
+83 25.488 -15.206 -14.025 62 86 23 4
+84 -32.643 8.787 0.154 78 21 88 53
+85 -14.534 16.274 -7.342 94 94 57 81
+86 26.704 -17.204 5.115 57 2 51 57
+87 -28.329 -16.516 -15.822 67 13 19 60
+88 13.571 -16.186 13.031 58 38 17 40
+89 -11.429 -2.984 16.068 5 36 50 18
+90 -35.700 5.003 -7.533 50 60 5 16
+91 30.834 -10.881 -7.255 1 24 47 77
+92 27.009 5.267 12.371 22 23 65 26
+93 26.504 9.561 9.008 54 78 76 94
+94 5.367 -1.449 2.623 59 26 52 63
+95 -29.551 15.440 -9.108 43 6 59 6
+96 -18.286 2.204 -16.040 16 77 81 49

From 74fe803996a504ca4265491ba02ec13c7a84678d Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 01:17:57 +0200
Subject: [PATCH 09/27] updated to have different order of releasing cameras

---
 .gitignore                               |   1 +
 docs/bundle_adjustment.md                |  26 ++
 openptv_python/demo_bundle_adjustment.py | 449 ++++++++++++++++++++++-
 openptv_python/orientation.py            | 337 +++++++++++++++--
 tests/test_bundle_adjustment.py          | 302 +++++++++++++++
 tests/test_demo_bundle_adjustment.py     |  50 +++
 6 files changed, 1126 insertions(+), 39 deletions(-)

diff --git a/.gitignore b/.gitignore
index 9727a52..5194d8e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -472,3 +472,4 @@ $RECYCLE.BIN/
 # End of https://www.toptal.com/developers/gitignore/api/python,jupyternotebooks,vim,visualstudiocode,pycharm,emacs,linux,macos,windows
 .cruft.json
 /.tmp
+/tmp
diff --git a/docs/bundle_adjustment.md b/docs/bundle_adjustment.md
index ee39fc0..59919d5 100644
--- a/docs/bundle_adjustment.md
+++ b/docs/bundle_adjustment.md
@@ -126,6 +126,7 @@ The current routines report and the demo prints:
 1. `initial_reprojection_rms` and `final_reprojection_rms`.
 1. Per-camera reprojection RMS for `multi_camera_bundle_adjustment`.
 1. `baseline_mean_ray_convergence`, `pose_mean_ray_convergence`, and `final_mean_ray_convergence` for guarded runs.
+1. `baseline_correspondence_rate`, `pose_correspondence_rate`, and `intrinsic_correspondence_rate` for guarded runs when original quadruplet identities are available.
 1. Runtime in seconds for each experiment.
 1. `known_point_indices` for constrained runs in `multi_camera_bundle_adjustment`.
 
@@ -165,6 +166,9 @@ The command-line options are:
 1. `--geometry-guard-mode {auto,off,soft,hard}`: control whether guarded two-step BA rejects stages that move a known 3D calibration target too far from a trusted reference calibration. `auto` enables `hard` when `parameters/cal_ori.par` points to a known 3D target file such as `cal/target_on_a_side.txt`.
 1. `--geometry-guard-threshold S`: pixel threshold used by `hard` geometry guards.
 1. `--geometry-export-threshold S`: refuse to write output case folders whose final calibration-body drift exceeds `S` pixels. Use `0` to disable export blocking.
+1. `--correspondence-guard-mode {auto,off,soft,hard}`: control whether guarded two-step BA rejects stages that replace too many original quadruplet target identities after reprojection and nearest-target reassignment. `auto` derives a threshold from the trusted reference calibration when tracking data is available.
+1. `--correspondence-guard-threshold S`: replacement-rate threshold used by `hard` correspondence guards.
+1. `--correspondence-export-threshold S`: refuse to write output case folders whose final correspondence replacement rate exceeds `S`. Use `0` to disable correspondence-based export blocking.
 
 ### Included Demo Presets
 
@@ -182,6 +186,12 @@ When a case exposes known 3D calibration-target points through `cal_ori.par`, th
 1. Output case folders are written only if the final calibration stays within the configured export drift threshold.
 1. Cases without a known 3D target file still run normally; the geometry guard simply stays off unless you explicitly enable it another way.
 
+When a case also exposes original tracked quadruplets and per-camera target files, the demo adds a correspondence-preserving workflow on top:
+
+1. Guarded presets can compare original target identities with the identities implied by the refined calibration.
+1. `auto` correspondence mode derives a threshold from the trusted reference calibration and rejects later stages that replace materially more quadruplets than that reference already does.
+1. Output case folders are skipped if the final replacement rate exceeds the configured correspondence export threshold.
+
 If `--known-points` is greater than zero, the demo also compares two constrained presets:
 
 1. `pose_trf_known_points`: pose-only bundle adjustment with soft 3D anchors.
@@ -236,6 +246,17 @@ python -m openptv_python.demo_bundle_adjustment \
   --output-dir .tmp/demo_bundle_adjustment_runs_soft
 ```
 
+Keep geometry blocking but also enforce a hard correspondence replacement limit derived from the trusted reference calibration:
+
+```bash
+python -m openptv_python.demo_bundle_adjustment \
+  tests/testing_fodder/test_cavity \
+  --max-frames 2 \
+  --max-points-per-frame 80 \
+  --correspondence-guard-mode auto \
+  --output-dir .tmp/demo_bundle_adjustment_runs_correspondence
+```
+
 Run without writing calibration folders:
 
 ```bash
@@ -290,12 +311,15 @@ tmp/bundle_adjustment_demo/
       ...
     calibration_delta.txt
     geometry_check.txt
+    correspondence_check.txt
 ```
 
 `calibration_delta.txt` is generated with `openptv_python.calibration_compare` and shows camera-by-camera parameter differences relative to the source case.
 
 `geometry_check.txt` reports per-camera drift of the known 3D calibration-body projections relative to the trusted reference calibration. If the final drift exceeds `--geometry-export-threshold`, the demo does not write that experiment's case folder at all.
 
+`correspondence_check.txt` reports how many original quadruplet target identities are replaced by the refined calibration after nearest-target reassignment. If the final replacement rate exceeds `--correspondence-export-threshold`, the demo does not write that experiment's case folder at all.
+
 ## Choosing Options
 
 These are the main tradeoffs when selecting settings.
@@ -353,6 +377,8 @@ This split is useful because the two diagnostics answer different questions:
 
 If epipolar distance stays bad while reprojection improves, the next step is usually not to add an epipolar term blindly. First identify whether a subset of camera pairs or quadruplets is driving the inconsistency, then decide whether to reject or downweight those observations.
 
+The correspondence guard is aimed at a related failure mode: a calibration can reduce RMS by drifting until many refined projections land closer to different detected targets than the original quadruplet used. In that case, the solution is numerically fitting the images better while no longer preserving the same tracked structure. The new replacement-rate check gives you an explicit acceptance and export criterion for that situation.
+
 ### Priors and Bounds
 
 1. `prior_sigmas` softly regularize motion away from the starting calibration.
diff --git a/openptv_python/demo_bundle_adjustment.py b/openptv_python/demo_bundle_adjustment.py
index ebc95f6..6aca494 100644
--- a/openptv_python/demo_bundle_adjustment.py
+++ b/openptv_python/demo_bundle_adjustment.py
@@ -17,7 +17,7 @@
     format_calibration_comparison,
 )
 from .epi import epipolar_curve
-from .imgcoord import img_coord
+from .imgcoord import image_coordinates, img_coord
 from .orientation import (
     guarded_two_step_bundle_adjustment,
     initialize_bundle_adjustment_points,
@@ -36,7 +36,7 @@
 )
 from .sortgrid import read_calblock as read_sortgrid_calblock
 from .tracking_frame_buf import read_path_frame, read_targets
-from .trafo import metric_to_pixel
+from .trafo import arr_metric_to_pixel, metric_to_pixel
 
 
 @dataclass(frozen=True)
@@ -67,6 +67,7 @@ class ExperimentResult:
     camera_position_shifts: List[float]
     camera_angle_shifts: List[float]
     geometry_projection_drift: List[ProjectionDriftSummary] | None = None
+    correspondence_replacement: CorrespondenceReplacementSummary | None = None
     refined_cals: List[Calibration] | None = None
     refined_points: np.ndarray | None = None
 
@@ -122,6 +123,27 @@ class ProjectionDriftSummary:
     max_distance: float
 
 
+@dataclass(frozen=True)
+class CorrespondenceReplacementSummary:
+    """How strongly a calibration would replace the original quadruplet identities."""
+
+    replacement_rate: float
+    camera_change_rates: List[float]
+    mean_nearest_distance: float
+    p95_nearest_distance: float
+    max_nearest_distance: float
+
+
+@dataclass(frozen=True)
+class ObservationTrackingData:
+    """Original correspondence identities and candidate detections for a case."""
+
+    original_target_ids: np.ndarray
+    point_frame_indices: np.ndarray
+    frame_target_pixels: List[List[np.ndarray]]
+    reference_replacement_rate: float | None = None
+
+
 def clone_calibration(cal: Calibration) -> Calibration:
     """Return a detached copy of a calibration object."""
     return Calibration(
@@ -377,6 +399,166 @@ def load_case_observations(
     )
 
 
+def summarize_correspondence_replacements(
+    points: np.ndarray,
+    cals: Sequence[Calibration],
+    control: ControlPar,
+    tracking_data: ObservationTrackingData | None,
+) -> CorrespondenceReplacementSummary | None:
+    """Measure how often reprojections switch to different target identities."""
+    if tracking_data is None:
+        return None
+
+    projected_pixels = np.empty((points.shape[0], len(cals), 2), dtype=float)
+    for camera_index, cal in enumerate(cals):
+        projected_pixels[:, camera_index, :] = arr_metric_to_pixel(
+            image_coordinates(points, cal, control.mm),
+            control,
+        )
+
+    replacement_ids = np.empty_like(tracking_data.original_target_ids)
+    nearest_distances = np.empty_like(tracking_data.original_target_ids, dtype=float)
+    for point_index in range(points.shape[0]):
+        frame_targets = tracking_data.frame_target_pixels[
+            int(tracking_data.point_frame_indices[point_index])
+        ]
+        for camera_index in range(len(cals)):
+            deltas = frame_targets[camera_index] - projected_pixels[point_index, camera_index]
+            squared_distances = np.sum(deltas * deltas, axis=1)
+            nearest_index = int(np.argmin(squared_distances))
+            replacement_ids[point_index, camera_index] = nearest_index
+            nearest_distances[point_index, camera_index] = float(
+                np.sqrt(squared_distances[nearest_index])
+            )
+
+    changed_mask = np.any(
+        replacement_ids != tracking_data.original_target_ids,
+        axis=1,
+    )
+    camera_change_rates = [
+        float(
+            np.mean(
+                replacement_ids[:, camera_index]
+                != tracking_data.original_target_ids[:, camera_index]
+            )
+        )
+        for camera_index in range(len(cals))
+    ]
+    return CorrespondenceReplacementSummary(
+        replacement_rate=float(np.mean(changed_mask)),
+        camera_change_rates=camera_change_rates,
+        mean_nearest_distance=float(np.mean(nearest_distances)),
+        p95_nearest_distance=float(np.percentile(nearest_distances, 95.0)),
+        max_nearest_distance=float(np.max(nearest_distances)),
+    )
+
+
+def load_case_tracking_data(
+    case_dir: Path,
+    num_cams: int,
+    *,
+    max_frames: int | None,
+    max_points_per_frame: int | None,
+    control: ControlPar,
+    reference_cals: Sequence[Calibration],
+) -> ObservationTrackingData | None:
+    """Load original target identities and candidate detections for replacement guards."""
+    seq = SequencePar.from_file(case_dir / "parameters/sequence.par", num_cams)
+    frames = list(range(seq.first, seq.last + 1))
+    if max_frames is not None:
+        frames = frames[:max_frames]
+
+    original_target_ids = []
+    point_frame_indices = []
+    frame_target_pixels: List[List[np.ndarray]] = []
+    reference_points = []
+    for frame_index, frame in enumerate(frames):
+        cor_buf, path_buf = read_path_frame(
+            str(case_dir / "res_orig/rt_is"),
+            "",
+            "",
+            frame,
+        )
+        targets = [
+            read_targets(str(case_dir / f"img_orig/cam{cam_num}.%05d"), frame)
+            for cam_num in range(1, num_cams + 1)
+        ]
+        subset = [
+            point_num
+            for point_num, corres in enumerate(cor_buf)
+            if np.all(corres.p[:num_cams] >= 0)
+        ]
+        if max_points_per_frame is not None:
+            subset = subset[:max_points_per_frame]
+
+        for point_num in subset:
+            original_target_ids.append(cor_buf[point_num].p[:num_cams].copy())
+            point_frame_indices.append(frame_index)
+            reference_points.append(path_buf[point_num].x.copy())
+
+        frame_target_pixels.append(
+            [np.asarray([[target.x, target.y] for target in cam_targets], dtype=float) for cam_targets in targets]
+        )
+
+    if not original_target_ids:
+        return None
+
+    tracking_data = ObservationTrackingData(
+        original_target_ids=np.asarray(original_target_ids, dtype=int),
+        point_frame_indices=np.asarray(point_frame_indices, dtype=int),
+        frame_target_pixels=frame_target_pixels,
+    )
+    reference_summary = summarize_correspondence_replacements(
+        np.asarray(reference_points, dtype=float),
+        reference_cals,
+        control,
+        tracking_data,
+    )
+    return ObservationTrackingData(
+        original_target_ids=tracking_data.original_target_ids,
+        point_frame_indices=tracking_data.point_frame_indices,
+        frame_target_pixels=tracking_data.frame_target_pixels,
+        reference_replacement_rate=(
+            None if reference_summary is None else reference_summary.replacement_rate
+        ),
+    )
+
+
+def format_correspondence_replacement(
+    summary: CorrespondenceReplacementSummary | None,
+) -> str:
+    """Render a compact correspondence-replacement summary."""
+    if summary is None:
+        return "No correspondence replacement data available."
+
+    camera_rates = " ".join(
+        f"cam{camera_index + 1}={100.0 * rate:.1f}%"
+        for camera_index, rate in enumerate(summary.camera_change_rates)
+    )
+    return (
+        f"quad_replacement_rate={100.0 * summary.replacement_rate:.1f}%\n"
+        f"camera_change_rates: {camera_rates}\n"
+        f"nearest_distance_px: mean={summary.mean_nearest_distance:.3f} "
+        f"p95={summary.p95_nearest_distance:.3f} max={summary.max_nearest_distance:.3f}"
+    )
+
+
+def should_block_export_on_correspondence(
+    summary: CorrespondenceReplacementSummary | None,
+    threshold: float | None,
+) -> tuple[bool, str]:
+    """Return whether an export should be blocked by correspondence churn."""
+    if summary is None or threshold is None or threshold <= 0:
+        return False, ""
+    if summary.replacement_rate <= threshold:
+        return False, ""
+    return (
+        True,
+        "correspondence_export_blocked="
+        f"replacement_rate={summary.replacement_rate:.3f}>{threshold:.3f}",
+    )
+
+
 def build_known_point_constraints(
     point_init: np.ndarray,
     count: int,
@@ -720,15 +902,46 @@ def summarize_fixed_camera_diagnostics(
     return diagnostics
 
 
+def normalize_staged_release_order(
+    staged_release_order: Sequence[int] | None,
+    num_cams: int,
+) -> List[int]:
+    """Return a validated zero-based staged camera release order."""
+    if staged_release_order is None:
+        order = list(range(num_cams))
+    else:
+        order = [int(camera_index) for camera_index in staged_release_order]
+
+    if len(order) != num_cams:
+        raise ValueError(
+            f"staged_release_order must contain exactly {num_cams} cameras"
+        )
+    if sorted(order) != list(range(num_cams)):
+        raise ValueError(
+            "staged_release_order must be a permutation of zero-based camera indices"
+        )
+    return order
+
+
 def default_experiments(
     *,
+    num_cams: int = 4,
     known_points: Dict[int, np.ndarray] | None = None,
     known_point_sigmas: float | np.ndarray | None = None,
     perturbation_scale: float = 1.0,
+    staged_release_order: Sequence[int] | None = None,
+    pose_stage_ray_slack: float = 0.0,
     geometry_guard_mode: str = "off",
     geometry_guard_threshold: float | None = None,
+    correspondence_guard_mode: str = "off",
+    correspondence_guard_threshold: float | None = None,
+    correspondence_guard_reference_rate: float | None = None,
 ) -> List[ExperimentSpec]:
     """Return a set of representative BA configurations."""
+    staged_order = normalize_staged_release_order(staged_release_order, num_cams)
+    staged_fixed = [
+        camera_index for camera_index in range(num_cams) if camera_index != staged_order[0]
+    ]
     pose_priors = {
         "x0": 0.5,
         "y0": 0.5,
@@ -831,7 +1044,7 @@ def default_experiments(
             mode="multi",
             ba_kwargs={
                 "orient_par": intrinsics_only,
-                "fixed_camera_indices": [0, 1, 2, 3],
+                "fixed_camera_indices": list(range(num_cams)),
                 "loss": "linear",
                 "method": "trf",
                 "prior_sigmas": {
@@ -868,6 +1081,32 @@ def default_experiments(
                 "intrinsic_max_nfev": 4,
                 "geometry_guard_mode": geometry_guard_mode,
                 "geometry_guard_threshold": geometry_guard_threshold,
+                "correspondence_guard_mode": correspondence_guard_mode,
+                "correspondence_guard_threshold": correspondence_guard_threshold,
+                "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
+            },
+        ),
+        ExperimentSpec(
+            name="guarded_stagewise_release",
+            description="Guarded BA that releases one camera at a time before tightly constrained intrinsics",
+            mode="guarded",
+            ba_kwargs={
+                "pose_orient_par": OrientPar(),
+                "intrinsic_orient_par": intrinsics,
+                "fixed_camera_indices": staged_fixed,
+                "pose_release_camera_order": staged_order,
+                "pose_stage_ray_slack": pose_stage_ray_slack,
+                "pose_prior_sigmas": pose_priors,
+                "pose_parameter_bounds": pose_bounds,
+                "pose_max_nfev": 8,
+                "intrinsic_prior_sigmas": tight_intrinsic_priors,
+                "intrinsic_parameter_bounds": tight_intrinsic_bounds,
+                "intrinsic_max_nfev": 4,
+                "geometry_guard_mode": geometry_guard_mode,
+                "geometry_guard_threshold": geometry_guard_threshold,
+                "correspondence_guard_mode": correspondence_guard_mode,
+                "correspondence_guard_threshold": correspondence_guard_threshold,
+                "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
             },
         ),
     ]
@@ -909,6 +1148,34 @@ def default_experiments(
                         "known_point_sigmas": known_point_sigmas,
                         "geometry_guard_mode": geometry_guard_mode,
                         "geometry_guard_threshold": geometry_guard_threshold,
+                        "correspondence_guard_mode": correspondence_guard_mode,
+                        "correspondence_guard_threshold": correspondence_guard_threshold,
+                        "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
+                    },
+                ),
+                ExperimentSpec(
+                    name="guarded_stagewise_release_known_points",
+                    description="Stagewise guarded BA with soft known-point geometry anchors",
+                    mode="guarded",
+                    ba_kwargs={
+                        "pose_orient_par": OrientPar(),
+                        "intrinsic_orient_par": intrinsics,
+                        "fixed_camera_indices": staged_fixed,
+                        "pose_release_camera_order": staged_order,
+                        "pose_stage_ray_slack": pose_stage_ray_slack,
+                        "pose_prior_sigmas": pose_priors,
+                        "pose_parameter_bounds": pose_bounds,
+                        "pose_max_nfev": 8,
+                        "intrinsic_prior_sigmas": tight_intrinsic_priors,
+                        "intrinsic_parameter_bounds": tight_intrinsic_bounds,
+                        "intrinsic_max_nfev": 4,
+                        "known_points": known_points,
+                        "known_point_sigmas": known_point_sigmas,
+                        "geometry_guard_mode": geometry_guard_mode,
+                        "geometry_guard_threshold": geometry_guard_threshold,
+                        "correspondence_guard_mode": correspondence_guard_mode,
+                        "correspondence_guard_threshold": correspondence_guard_threshold,
+                        "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
                     },
                 ),
             ]
@@ -942,7 +1209,9 @@ def run_experiment(
     start_cals: List[Calibration],
     reference_cals: List[Calibration],
     reference_geometry_points: np.ndarray | None,
+    tracking_data: ObservationTrackingData | None,
     geometry_export_threshold: float | None,
+    correspondence_export_threshold: float | None,
     source_case_dir: Path,
     output_dir: Path | None,
 ) -> ExperimentResult:
@@ -1020,6 +1289,14 @@ def run_experiment(
                 List[int] | None,
                 spec.ba_kwargs.get("fixed_camera_indices"),
             ),
+            pose_release_camera_order=cast(
+                List[int] | None,
+                spec.ba_kwargs.get("pose_release_camera_order"),
+            ),
+            pose_stage_ray_slack=cast(
+                float,
+                spec.ba_kwargs.get("pose_stage_ray_slack", 0.0),
+            ),
             pose_prior_sigmas=cast(
                 Dict[str, float] | None,
                 spec.ba_kwargs.get("pose_prior_sigmas"),
@@ -1094,6 +1371,27 @@ def run_experiment(
                 float | None,
                 spec.ba_kwargs.get("geometry_guard_threshold"),
             ),
+            correspondence_original_ids=(
+                None if tracking_data is None else tracking_data.original_target_ids
+            ),
+            correspondence_point_frame_indices=(
+                None if tracking_data is None else tracking_data.point_frame_indices
+            ),
+            correspondence_frame_target_pixels=(
+                None if tracking_data is None else tracking_data.frame_target_pixels
+            ),
+            correspondence_guard_mode=cast(
+                str,
+                spec.ba_kwargs.get("correspondence_guard_mode", "off"),
+            ),
+            correspondence_guard_threshold=cast(
+                float | None,
+                spec.ba_kwargs.get("correspondence_guard_threshold"),
+            ),
+            correspondence_guard_reference_rate=cast(
+                float | None,
+                spec.ba_kwargs.get("correspondence_guard_reference_rate"),
+            ),
         )
         success = True
         final_rms = cast(float, summary["final_reprojection_rms"])
@@ -1113,17 +1411,29 @@ def run_experiment(
         control,
         reference_geometry_points,
     )
+    correspondence_replacement = summarize_correspondence_replacements(
+        refined_points,
+        refined_cals,
+        control,
+        tracking_data,
+    )
     export_blocked, export_note = should_block_export_on_geometry(
         geometry_projection_drift,
         geometry_export_threshold,
     )
+    correspondence_blocked, correspondence_note = should_block_export_on_correspondence(
+        correspondence_replacement,
+        correspondence_export_threshold,
+    )
     if export_note:
         notes = f"{notes}; {export_note}" if notes else export_note
-    if export_blocked:
+    if correspondence_note:
+        notes = f"{notes}; {correspondence_note}" if notes else correspondence_note
+    if export_blocked or correspondence_blocked:
         success = False
 
     cal_dir = None
-    if output_dir is not None and not export_blocked:
+    if output_dir is not None and not (export_blocked or correspondence_blocked):
         case_out_dir = output_dir / spec.name
         cal_dir = ensure_output_case_layout(source_case_dir, case_out_dir)
         write_calibration_folder(refined_cals, cal_dir)
@@ -1141,6 +1451,10 @@ def run_experiment(
             format_projection_drift(geometry_projection_drift) + "\n",
             encoding="utf-8",
         )
+        (case_out_dir / "correspondence_check.txt").write_text(
+            format_correspondence_replacement(correspondence_replacement) + "\n",
+            encoding="utf-8",
+        )
 
     return ExperimentResult(
         name=spec.name,
@@ -1157,6 +1471,7 @@ def run_experiment(
         camera_position_shifts=camera_position_shifts,
         camera_angle_shifts=camera_angle_shifts,
         geometry_projection_drift=geometry_projection_drift,
+        correspondence_replacement=correspondence_replacement,
         refined_cals=refined_cals,
         refined_points=refined_points,
     )
@@ -1312,7 +1627,9 @@ def run_fixed_camera_pair_diagnostics(
     start_cals: List[Calibration],
     reference_cals: List[Calibration],
     reference_geometry_points: np.ndarray | None,
+    tracking_data: ObservationTrackingData | None,
     geometry_export_threshold: float | None,
+    correspondence_export_threshold: float | None,
     source_case_dir: Path,
 ) -> List[FixedCameraDiagnostic]:
     """Run one experiment over every two-camera anchor pair and summarize results."""
@@ -1335,7 +1652,9 @@ def run_fixed_camera_pair_diagnostics(
                 start_cals=start_cals,
                 reference_cals=reference_cals,
                 reference_geometry_points=reference_geometry_points,
+                tracking_data=tracking_data,
                 geometry_export_threshold=geometry_export_threshold,
+                correspondence_export_threshold=correspondence_export_threshold,
                 source_case_dir=source_case_dir,
                 output_dir=None,
             )
@@ -1440,9 +1759,40 @@ def parse_args(argv: Iterable[str] | None = None) -> argparse.Namespace:
     parser.add_argument(
         "--geometry-export-threshold",
         type=float,
-        default=2.5,
+        default=None,
         help="Do not write output case folders whose final calibration-body drift exceeds this many pixels; 0 disables export blocking.",
     )
+    parser.add_argument(
+        "--correspondence-guard-mode",
+        type=str,
+        choices=("auto", "off", "soft", "hard"),
+        default="auto",
+        help="Guarded-BA correspondence acceptance mode. 'auto' uses a hard replacement-rate guard when original quadruplet identities are available.",
+    )
+    parser.add_argument(
+        "--correspondence-guard-threshold",
+        type=float,
+        default=None,
+        help="Maximum allowed quadruplet replacement rate for hard correspondence guards. If omitted, the demo derives a case-specific threshold from the trusted reference calibration.",
+    )
+    parser.add_argument(
+        "--correspondence-export-threshold",
+        type=float,
+        default=None,
+        help="Do not write output case folders whose final quadruplet replacement rate exceeds this threshold; 0 disables correspondence-based export blocking.",
+    )
+    parser.add_argument(
+        "--staged-release-order",
+        type=str,
+        default=None,
+        help="Comma-separated 1-based camera release order for staged guarded presets, for example '1,2,3,4'.",
+    )
+    parser.add_argument(
+        "--staged-ray-slack",
+        type=float,
+        default=0.0,
+        help="Allow staged guarded pose-release steps to worsen mean ray convergence by up to this amount relative to the last accepted stage.",
+    )
     return parser.parse_args(list(argv) if argv is not None else None)
 
 
@@ -1461,12 +1811,68 @@ def main(argv: Iterable[str] | None = None) -> int:
     true_cals = load_calibrations(case_dir, num_cams)
     start_cals = perturb_calibrations(true_cals, args.perturbation_scale)
     reference_geometry_points = load_reference_geometry_points(case_dir, num_cams)
+    tracking_data = load_case_tracking_data(
+        case_dir,
+        num_cams,
+        max_frames=args.max_frames,
+        max_points_per_frame=args.max_points_per_frame,
+        control=control,
+        reference_cals=true_cals,
+    )
     if args.geometry_guard_mode == "auto":
         geometry_guard_mode = "hard" if reference_geometry_points is not None else "off"
     else:
         geometry_guard_mode = args.geometry_guard_mode
     geometry_export_threshold = (
-        args.geometry_export_threshold if reference_geometry_points is not None else None
+        (
+            args.geometry_export_threshold
+            if args.geometry_export_threshold is not None
+            else args.geometry_guard_threshold
+        )
+        if reference_geometry_points is not None
+        else None
+    )
+    if args.correspondence_guard_mode == "auto":
+        correspondence_guard_mode = "hard" if tracking_data is not None else "off"
+    else:
+        correspondence_guard_mode = args.correspondence_guard_mode
+    if tracking_data is not None and tracking_data.reference_replacement_rate is not None:
+        auto_correspondence_threshold = min(
+            0.25,
+            max(0.05, tracking_data.reference_replacement_rate + 0.02),
+        )
+    else:
+        auto_correspondence_threshold = None
+    correspondence_guard_threshold = (
+        args.correspondence_guard_threshold
+        if args.correspondence_guard_threshold is not None
+        else auto_correspondence_threshold
+    )
+    correspondence_export_threshold = (
+        (
+            args.correspondence_export_threshold
+            if args.correspondence_export_threshold is not None
+            else correspondence_guard_threshold
+        )
+        if tracking_data is not None
+        else None
+    )
+    if args.staged_release_order is None:
+        staged_release_order = list(range(num_cams))
+    else:
+        try:
+            staged_release_order = [
+                int(token.strip()) - 1
+                for token in args.staged_release_order.split(",")
+                if token.strip()
+            ]
+        except ValueError as exc:
+            raise ValueError(
+                "--staged-release-order must be a comma-separated list of integers"
+            ) from exc
+    staged_release_order = normalize_staged_release_order(
+        staged_release_order,
+        num_cams,
     )
     known_points = build_known_point_constraints(point_init, args.known_points)
     known_point_sigmas = args.known_point_sigma if known_points else None
@@ -1488,19 +1894,40 @@ def main(argv: Iterable[str] | None = None) -> int:
         print("Known-point anchors: disabled")
     if reference_geometry_points is not None:
         print(
-            f"Geometry guard: mode={geometry_guard_mode}, guard_threshold={args.geometry_guard_threshold:.3f}px, export_threshold={args.geometry_export_threshold:.3f}px"
+            f"Geometry guard: mode={geometry_guard_mode}, guard_threshold={args.geometry_guard_threshold:.3f}px, export_threshold={geometry_export_threshold:.3f}px"
         )
     else:
         print("Geometry guard: unavailable for this case (no known 3D target file found)")
+    if tracking_data is not None and tracking_data.reference_replacement_rate is not None:
+        print(
+            "Correspondence guard: "
+            f"mode={correspondence_guard_mode}, reference_rate={tracking_data.reference_replacement_rate:.3f}, "
+            f"guard_threshold={correspondence_guard_threshold:.3f}, export_threshold={correspondence_export_threshold:.3f}"
+        )
+    else:
+        print("Correspondence guard: unavailable for this case")
+    print(
+        "Staged guarded release: "
+        f"order={[camera_index + 1 for camera_index in staged_release_order]}, "
+        f"ray_slack={args.staged_ray_slack:.6f}"
+    )
     print(f"Output folders: {output_dir if output_dir is not None else 'disabled'}")
     print()
 
     experiments = default_experiments(
+        num_cams=num_cams,
         known_points=known_points or None,
         known_point_sigmas=known_point_sigmas,
         perturbation_scale=args.perturbation_scale,
+        staged_release_order=staged_release_order,
+        pose_stage_ray_slack=args.staged_ray_slack,
         geometry_guard_mode=geometry_guard_mode,
         geometry_guard_threshold=args.geometry_guard_threshold,
+        correspondence_guard_mode=correspondence_guard_mode,
+        correspondence_guard_threshold=correspondence_guard_threshold,
+        correspondence_guard_reference_rate=(
+            None if tracking_data is None else tracking_data.reference_replacement_rate
+        ),
     )
 
     if args.diagnose_fixed_pairs:
@@ -1513,7 +1940,9 @@ def main(argv: Iterable[str] | None = None) -> int:
             start_cals=start_cals,
             reference_cals=true_cals,
             reference_geometry_points=reference_geometry_points,
+            tracking_data=tracking_data,
             geometry_export_threshold=geometry_export_threshold,
+            correspondence_export_threshold=correspondence_export_threshold,
             source_case_dir=case_dir,
         )
         print(
@@ -1540,7 +1969,9 @@ def main(argv: Iterable[str] | None = None) -> int:
             start_cals=start_cals,
             reference_cals=true_cals,
             reference_geometry_points=reference_geometry_points,
+            tracking_data=tracking_data,
             geometry_export_threshold=geometry_export_threshold,
+            correspondence_export_threshold=correspondence_export_threshold,
             source_case_dir=case_dir,
             output_dir=None,
         )
@@ -1606,7 +2037,9 @@ def main(argv: Iterable[str] | None = None) -> int:
             start_cals=start_cals,
             reference_cals=true_cals,
             reference_geometry_points=reference_geometry_points,
+            tracking_data=tracking_data,
             geometry_export_threshold=geometry_export_threshold,
+            correspondence_export_threshold=correspondence_export_threshold,
             source_case_dir=case_dir,
             output_dir=output_dir,
         )
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index 0e22115..ada532e 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -1,6 +1,6 @@
 """Functions for the orientation of the camera."""
 
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Sequence, Tuple, cast
 
 import numpy as np
 import scipy
@@ -1751,6 +1751,8 @@ def guarded_two_step_bundle_adjustment(
     *,
     point_init: Optional[np.ndarray] = None,
     fixed_camera_indices: Optional[List[int]] = None,
+    pose_release_camera_order: Optional[List[int]] = None,
+    pose_stage_ray_slack: float = 0.0,
     pose_prior_sigmas: Optional[Dict[str, float]] = None,
     pose_parameter_bounds: Optional[Dict[str, Tuple[float, float]]] = None,
     pose_loss: str = "linear",
@@ -1772,11 +1774,20 @@ def guarded_two_step_bundle_adjustment(
     geometry_reference_cals: Optional[List[Calibration]] = None,
     geometry_guard_mode: str = "off",
     geometry_guard_threshold: Optional[float] = None,
+    correspondence_original_ids: Optional[np.ndarray] = None,
+    correspondence_point_frame_indices: Optional[np.ndarray] = None,
+    correspondence_frame_target_pixels: Optional[Sequence[Sequence[np.ndarray]]] = None,
+    correspondence_guard_mode: str = "off",
+    correspondence_guard_threshold: Optional[float] = None,
+    correspondence_guard_reference_rate: Optional[float] = None,
     reject_worse_solution: bool = True,
     reject_on_ray_convergence: bool = True,
 ) -> Tuple[List[Calibration], np.ndarray, Dict[str, object]]:
     """Run pose-only BA then tightly constrained intrinsics BA with acceptance checks."""
 
+    if pose_stage_ray_slack < 0:
+        raise ValueError("pose_stage_ray_slack must be non-negative")
+
     def projection_drift_summaries(
         reference_cals: List[Calibration],
         candidate_cals: List[Calibration],
@@ -1840,6 +1851,88 @@ def geometry_stage_ok(
             "geometry_guard_mode must be one of 'off', 'soft', or 'hard'"
         )
 
+    def correspondence_replacement_summary(
+        candidate_points: np.ndarray,
+        candidate_cals: List[Calibration],
+    ) -> Optional[Dict[str, object]]:
+        if (
+            correspondence_original_ids is None
+            or correspondence_point_frame_indices is None
+            or correspondence_frame_target_pixels is None
+        ):
+            return None
+
+        projected_pixels = np.empty(
+            (candidate_points.shape[0], len(candidate_cals), 2),
+            dtype=np.float64,
+        )
+        for camera_index, cal in enumerate(candidate_cals):
+            projected_pixels[:, camera_index, :] = arr_metric_to_pixel(
+                image_coordinates(candidate_points, cal, cpar.mm),
+                cpar,
+            )
+
+        replacement_ids = np.empty_like(correspondence_original_ids)
+        nearest_distances = np.empty_like(
+            correspondence_original_ids,
+            dtype=np.float64,
+        )
+        for point_index in range(candidate_points.shape[0]):
+            frame_targets = correspondence_frame_target_pixels[
+                int(correspondence_point_frame_indices[point_index])
+            ]
+            for camera_index in range(len(candidate_cals)):
+                deltas = frame_targets[camera_index] - projected_pixels[point_index, camera_index]
+                squared_distances = np.sum(deltas * deltas, axis=1)
+                nearest_index = int(np.argmin(squared_distances))
+                replacement_ids[point_index, camera_index] = nearest_index
+                nearest_distances[point_index, camera_index] = float(
+                    np.sqrt(squared_distances[nearest_index])
+                )
+
+        changed_mask = np.any(
+            replacement_ids != correspondence_original_ids,
+            axis=1,
+        )
+        camera_change_rates = [
+            float(
+                np.mean(
+                    replacement_ids[:, camera_index]
+                    != correspondence_original_ids[:, camera_index]
+                )
+            )
+            for camera_index in range(len(candidate_cals))
+        ]
+        return {
+            "replacement_rate": float(np.mean(changed_mask)),
+            "camera_change_rates": camera_change_rates,
+            "mean_nearest_distance": float(np.mean(nearest_distances)),
+            "p95_nearest_distance": float(np.percentile(nearest_distances, 95.0)),
+            "max_nearest_distance": float(np.max(nearest_distances)),
+        }
+
+    def correspondence_stage_ok(
+        candidate_rate: Optional[float],
+        prior_rate: Optional[float],
+    ) -> bool:
+        if correspondence_guard_mode == "off" or candidate_rate is None:
+            return True
+        if correspondence_guard_mode == "soft":
+            if correspondence_guard_reference_rate is not None:
+                return candidate_rate <= correspondence_guard_reference_rate + 1e-12
+            if prior_rate is None:
+                return True
+            return candidate_rate <= prior_rate + 1e-12
+        if correspondence_guard_mode == "hard":
+            if correspondence_guard_threshold is None or correspondence_guard_threshold <= 0:
+                raise ValueError(
+                    "correspondence_guard_threshold must be positive when correspondence_guard_mode='hard'"
+                )
+            return candidate_rate <= correspondence_guard_threshold
+        raise ValueError(
+            "correspondence_guard_mode must be one of 'off', 'soft', or 'hard'"
+        )
+
     base_cals = [_clone_calibration(cal) for cal in cals]
     if point_init is None:
         base_points, _ = initialize_bundle_adjustment_points(
@@ -1861,32 +1954,190 @@ def geometry_stage_ok(
         geometry_reference_points,
     )
     baseline_geometry_max = max_projection_drift(baseline_geometry)
-
-    pose_cals, pose_points, pose_result = multi_camera_bundle_adjustment(
-        observed_pixels,
+    baseline_correspondence = correspondence_replacement_summary(
+        base_points,
         base_cals,
-        cpar,
-        pose_orient_par,
-        point_init=base_points,
-        fixed_camera_indices=fixed_camera_indices,
-        loss=pose_loss,
-        method=pose_method,
-        prior_sigmas=pose_prior_sigmas,
-        parameter_bounds=pose_parameter_bounds,
-        max_nfev=pose_max_nfev,
-        optimize_extrinsics=True,
-        optimize_points=pose_optimize_points,
-        known_points=known_points,
-        known_point_sigmas=known_point_sigmas,
     )
-    pose_rms = reprojection_rms(observed_pixels, pose_points, pose_cals, cpar)
-    pose_ray_convergence = mean_ray_convergence(observed_pixels, pose_cals, cpar)
-    pose_geometry = projection_drift_summaries(
-        geometry_reference_cals,
-        pose_cals,
-        geometry_reference_points,
+    baseline_correspondence_rate = (
+        None
+        if baseline_correspondence is None
+        else cast(float, baseline_correspondence["replacement_rate"])
     )
-    pose_geometry_max = max_projection_drift(pose_geometry)
+
+    pose_stage_summaries: List[Dict[str, object]] = []
+    pose_result: object
+    pose_ok: bool
+    if pose_release_camera_order is None:
+        pose_cals, pose_points, pose_result = multi_camera_bundle_adjustment(
+            observed_pixels,
+            base_cals,
+            cpar,
+            pose_orient_par,
+            point_init=base_points,
+            fixed_camera_indices=fixed_camera_indices,
+            loss=pose_loss,
+            method=pose_method,
+            prior_sigmas=pose_prior_sigmas,
+            parameter_bounds=pose_parameter_bounds,
+            max_nfev=pose_max_nfev,
+            optimize_extrinsics=True,
+            optimize_points=pose_optimize_points,
+            known_points=known_points,
+            known_point_sigmas=known_point_sigmas,
+        )
+        pose_rms = reprojection_rms(observed_pixels, pose_points, pose_cals, cpar)
+        pose_ray_convergence = mean_ray_convergence(observed_pixels, pose_cals, cpar)
+        pose_geometry = projection_drift_summaries(
+            geometry_reference_cals,
+            pose_cals,
+            geometry_reference_points,
+        )
+        pose_geometry_max = max_projection_drift(pose_geometry)
+        pose_correspondence = correspondence_replacement_summary(
+            pose_points,
+            pose_cals,
+        )
+        pose_correspondence_rate = (
+            None
+            if pose_correspondence is None
+            else cast(float, pose_correspondence["replacement_rate"])
+        )
+        pose_ok = pose_rms <= baseline_rms and (
+            not reject_on_ray_convergence
+            or pose_ray_convergence <= baseline_ray_convergence
+        )
+        pose_geometry_ok = geometry_stage_ok(pose_geometry_max, baseline_geometry_max)
+        pose_ok = pose_ok and pose_geometry_ok
+        pose_correspondence_ok = correspondence_stage_ok(
+            pose_correspondence_rate,
+            baseline_correspondence_rate,
+        )
+        pose_ok = pose_ok and pose_correspondence_ok
+    else:
+        release_order = [int(camera_index) for camera_index in pose_release_camera_order]
+        if not release_order:
+            raise ValueError("pose_release_camera_order must not be empty")
+        if len(set(release_order)) != len(release_order):
+            raise ValueError("pose_release_camera_order must not contain duplicates")
+        if any(camera_index < 0 or camera_index >= len(cals) for camera_index in release_order):
+            raise ValueError(
+                "pose_release_camera_order contains an out-of-range camera index"
+            )
+
+        current_cals = [_clone_calibration(cal) for cal in base_cals]
+        current_points = np.asarray(base_points, dtype=np.float64).copy()
+        current_rms = baseline_rms
+        current_ray_convergence = baseline_ray_convergence
+        current_geometry = baseline_geometry
+        current_geometry_max = baseline_geometry_max
+        current_correspondence = baseline_correspondence
+        current_correspondence_rate = baseline_correspondence_rate
+        pose_result = {"staged": True, "stages": []}
+        pose_ok = False
+        pose_geometry_ok = True
+        pose_correspondence_ok = True
+        released_cameras: List[int] = []
+
+        for stage_index, released_camera in enumerate(release_order, start=1):
+            released_cameras.append(released_camera)
+            stage_fixed = [
+                camera_index
+                for camera_index in range(len(cals))
+                if camera_index not in released_cameras
+            ]
+            stage_cals, stage_points, stage_result = multi_camera_bundle_adjustment(
+                observed_pixels,
+                current_cals,
+                cpar,
+                pose_orient_par,
+                point_init=current_points,
+                fixed_camera_indices=stage_fixed,
+                loss=pose_loss,
+                method=pose_method,
+                prior_sigmas=pose_prior_sigmas,
+                parameter_bounds=pose_parameter_bounds,
+                max_nfev=pose_max_nfev,
+                optimize_extrinsics=True,
+                optimize_points=pose_optimize_points,
+                known_points=known_points,
+                known_point_sigmas=known_point_sigmas,
+            )
+            stage_rms = reprojection_rms(observed_pixels, stage_points, stage_cals, cpar)
+            stage_ray_convergence = mean_ray_convergence(
+                observed_pixels, stage_cals, cpar
+            )
+            stage_geometry = projection_drift_summaries(
+                geometry_reference_cals,
+                stage_cals,
+                geometry_reference_points,
+            )
+            stage_geometry_max = max_projection_drift(stage_geometry)
+            stage_correspondence = correspondence_replacement_summary(
+                stage_points,
+                stage_cals,
+            )
+            stage_correspondence_rate = (
+                None
+                if stage_correspondence is None
+                else cast(float, stage_correspondence["replacement_rate"])
+            )
+            stage_geometry_ok = geometry_stage_ok(
+                stage_geometry_max,
+                current_geometry_max,
+            )
+            stage_correspondence_ok = correspondence_stage_ok(
+                stage_correspondence_rate,
+                current_correspondence_rate,
+            )
+            stage_ok = stage_rms <= current_rms and (
+                not reject_on_ray_convergence
+                or stage_ray_convergence
+                <= current_ray_convergence + pose_stage_ray_slack
+            )
+            stage_ok = stage_ok and stage_geometry_ok and stage_correspondence_ok
+            pose_stage_summaries.append(
+                {
+                    "stage_index": stage_index,
+                    "released_camera_index": released_camera,
+                    "free_camera_indices": released_cameras.copy(),
+                    "fixed_camera_indices": stage_fixed,
+                    "reprojection_rms": stage_rms,
+                    "mean_ray_convergence": stage_ray_convergence,
+                    "geometry": stage_geometry,
+                    "geometry_max": stage_geometry_max,
+                    "geometry_ok": stage_geometry_ok,
+                    "correspondence": stage_correspondence,
+                    "correspondence_rate": stage_correspondence_rate,
+                    "correspondence_ok": stage_correspondence_ok,
+                    "accepted": stage_ok or not reject_worse_solution,
+                    "result": stage_result,
+                }
+            )
+            cast(List[object], pose_result["stages"]).append(stage_result)
+
+            if reject_worse_solution and not stage_ok:
+                break
+
+            current_cals = stage_cals
+            current_points = stage_points
+            current_rms = stage_rms
+            current_ray_convergence = stage_ray_convergence
+            current_geometry = stage_geometry
+            current_geometry_max = stage_geometry_max
+            current_correspondence = stage_correspondence
+            current_correspondence_rate = stage_correspondence_rate
+            pose_ok = True
+            pose_geometry_ok = stage_geometry_ok
+            pose_correspondence_ok = stage_correspondence_ok
+
+        pose_cals = current_cals
+        pose_points = current_points
+        pose_rms = current_rms
+        pose_ray_convergence = current_ray_convergence
+        pose_geometry = current_geometry
+        pose_geometry_max = current_geometry_max
+        pose_correspondence = current_correspondence
+        pose_correspondence_rate = current_correspondence_rate
 
     intrinsic_fixed = list(range(len(cals)))
     intrinsic_cals, intrinsic_points, intrinsic_result = multi_camera_bundle_adjustment(
@@ -1921,19 +2172,21 @@ def geometry_stage_ok(
         geometry_reference_points,
     )
     intrinsic_geometry_max = max_projection_drift(intrinsic_geometry)
+    intrinsic_correspondence = correspondence_replacement_summary(
+        intrinsic_points,
+        intrinsic_cals,
+    )
+    intrinsic_correspondence_rate = (
+        None
+        if intrinsic_correspondence is None
+        else cast(float, intrinsic_correspondence["replacement_rate"])
+    )
 
     accepted_stage = "intrinsics"
     final_cals = intrinsic_cals
     final_points = intrinsic_points
     final_rms = intrinsic_rms
     final_ray_convergence = intrinsic_ray_convergence
-
-    pose_ok = pose_rms <= baseline_rms and (
-        not reject_on_ray_convergence
-        or pose_ray_convergence <= baseline_ray_convergence
-    )
-    pose_geometry_ok = geometry_stage_ok(pose_geometry_max, baseline_geometry_max)
-    pose_ok = pose_ok and pose_geometry_ok
     intrinsic_ok = intrinsic_rms <= pose_rms and (
         not reject_on_ray_convergence
         or intrinsic_ray_convergence <= pose_ray_convergence
@@ -1943,6 +2196,11 @@ def geometry_stage_ok(
         pose_geometry_max,
     )
     intrinsic_ok = intrinsic_ok and intrinsic_geometry_ok
+    intrinsic_correspondence_ok = correspondence_stage_ok(
+        intrinsic_correspondence_rate,
+        pose_correspondence_rate,
+    )
+    intrinsic_ok = intrinsic_ok and intrinsic_correspondence_ok
 
     if reject_worse_solution:
         if not pose_ok:
@@ -1965,6 +2223,8 @@ def geometry_stage_ok(
         "baseline_points": base_points,
         "baseline_geometry": baseline_geometry,
         "baseline_geometry_max": baseline_geometry_max,
+        "baseline_correspondence": baseline_correspondence,
+        "baseline_correspondence_rate": baseline_correspondence_rate,
         "pose_reprojection_rms": pose_rms,
         "pose_mean_ray_convergence": pose_ray_convergence,
         "pose_cals": pose_cals,
@@ -1972,6 +2232,15 @@ def geometry_stage_ok(
         "pose_geometry": pose_geometry,
         "pose_geometry_max": pose_geometry_max,
         "pose_geometry_ok": pose_geometry_ok,
+        "pose_release_camera_order": pose_release_camera_order,
+        "pose_stage_ray_slack": pose_stage_ray_slack,
+        "pose_stage_summaries": pose_stage_summaries,
+        "accepted_pose_stage_count": len(
+            [summary for summary in pose_stage_summaries if summary["accepted"]]
+        ),
+        "pose_correspondence": pose_correspondence,
+        "pose_correspondence_rate": pose_correspondence_rate,
+        "pose_correspondence_ok": pose_correspondence_ok,
         "intrinsic_reprojection_rms": intrinsic_rms,
         "intrinsic_mean_ray_convergence": intrinsic_ray_convergence,
         "intrinsic_cals": intrinsic_cals,
@@ -1979,8 +2248,14 @@ def geometry_stage_ok(
         "intrinsic_geometry": intrinsic_geometry,
         "intrinsic_geometry_max": intrinsic_geometry_max,
         "intrinsic_geometry_ok": intrinsic_geometry_ok,
+        "intrinsic_correspondence": intrinsic_correspondence,
+        "intrinsic_correspondence_rate": intrinsic_correspondence_rate,
+        "intrinsic_correspondence_ok": intrinsic_correspondence_ok,
         "geometry_guard_mode": geometry_guard_mode,
         "geometry_guard_threshold": geometry_guard_threshold,
+        "correspondence_guard_mode": correspondence_guard_mode,
+        "correspondence_guard_threshold": correspondence_guard_threshold,
+        "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
         "accepted_stage": accepted_stage,
         "final_reprojection_rms": final_rms,
         "final_mean_ray_convergence": final_ray_convergence,
diff --git a/tests/test_bundle_adjustment.py b/tests/test_bundle_adjustment.py
index fad2c68..a146b70 100644
--- a/tests/test_bundle_adjustment.py
+++ b/tests/test_bundle_adjustment.py
@@ -644,6 +644,162 @@ def test_guarded_two_step_bundle_adjustment_passes_known_point_constraints(self)
             self.assertEqual(call.kwargs["known_points"], known_points)
             self.assertEqual(call.kwargs["known_point_sigmas"], 1e-3)
 
+    def test_guarded_two_step_bundle_adjustment_supports_staged_camera_release(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        start_cals = self.lightly_perturb_calibrations(self.true_cals)
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+
+        staged_returns = [
+            (self.true_cals, points.copy(), {"success": True, "stage": f"pose_{idx}"})
+            for idx in range(4)
+        ] + [
+            (self.true_cals, points.copy(), {"success": True, "stage": "intrinsics"})
+        ]
+
+        with patch(
+            "openptv_python.orientation.multi_camera_bundle_adjustment",
+            side_effect=staged_returns,
+        ) as mocked_adjustment, patch(
+            "openptv_python.orientation.reprojection_rms",
+            side_effect=[10.0, 9.0, 8.0, 7.0, 6.0, 5.0],
+        ), patch(
+            "openptv_python.orientation.mean_ray_convergence",
+            side_effect=[6.0, 5.0, 4.0, 3.0, 2.0, 1.0],
+        ):
+            _, final_points, summary = guarded_two_step_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                intrinsics,
+                point_init=points,
+                pose_release_camera_order=[0, 1, 2, 3],
+                pose_prior_sigmas={
+                    "x0": 0.5,
+                    "y0": 0.5,
+                    "z0": 0.5,
+                    "omega": 0.005,
+                    "phi": 0.005,
+                    "kappa": 0.005,
+                },
+                pose_parameter_bounds={
+                    "x0": (-2.0, 2.0),
+                    "y0": (-2.0, 2.0),
+                    "z0": (-2.0, 2.0),
+                    "omega": (-0.02, 0.02),
+                    "phi": (-0.02, 0.02),
+                    "kappa": (-0.02, 0.02),
+                },
+                pose_max_nfev=20,
+                intrinsic_max_nfev=10,
+            )
+
+        self.assertEqual(mocked_adjustment.call_count, 5)
+        fixed_sequences = [
+            call.kwargs.get("fixed_camera_indices")
+            for call in mocked_adjustment.call_args_list
+        ]
+        self.assertEqual(
+            fixed_sequences,
+            [[1, 2, 3], [2, 3], [3], [], [0, 1, 2, 3]],
+        )
+        self.assertEqual(summary["pose_release_camera_order"], [0, 1, 2, 3])
+        self.assertEqual(summary["accepted_pose_stage_count"], 4)
+        self.assertEqual(summary["accepted_stage"], "intrinsics")
+        self.assertEqual(len(summary["pose_stage_summaries"]), 4)
+        self.assertEqual(
+            [stage["released_camera_index"] for stage in summary["pose_stage_summaries"]],
+            [0, 1, 2, 3],
+        )
+        self.assertEqual(final_points.shape, points.shape)
+
+    def test_guarded_two_step_bundle_adjustment_stagewise_ray_slack_allows_near_miss(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        start_cals = self.lightly_perturb_calibrations(self.true_cals)
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+
+        staged_returns = [
+            (self.true_cals, points.copy(), {"success": True, "stage": f"pose_{idx}"})
+            for idx in range(4)
+        ] + [
+            (self.true_cals, points.copy(), {"success": True, "stage": "intrinsics"})
+        ]
+
+        with patch(
+            "openptv_python.orientation.multi_camera_bundle_adjustment",
+            side_effect=staged_returns,
+        ), patch(
+            "openptv_python.orientation.reprojection_rms",
+            side_effect=[10.0, 9.0, 8.0, 7.0, 6.0, 5.5],
+        ), patch(
+            "openptv_python.orientation.mean_ray_convergence",
+            side_effect=[6.0, 5.0, 4.0, 4.0005, 3.5, 3.4],
+        ):
+            _, _, summary = guarded_two_step_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                intrinsics,
+                point_init=points,
+                pose_release_camera_order=[0, 1, 2, 3],
+                pose_stage_ray_slack=1e-3,
+                pose_prior_sigmas={
+                    "x0": 0.5,
+                    "y0": 0.5,
+                    "z0": 0.5,
+                    "omega": 0.005,
+                    "phi": 0.005,
+                    "kappa": 0.005,
+                },
+                pose_parameter_bounds={
+                    "x0": (-2.0, 2.0),
+                    "y0": (-2.0, 2.0),
+                    "z0": (-2.0, 2.0),
+                    "omega": (-0.02, 0.02),
+                    "phi": (-0.02, 0.02),
+                    "kappa": (-0.02, 0.02),
+                },
+            )
+
+        self.assertEqual(summary["accepted_pose_stage_count"], 4)
+        self.assertEqual(summary["pose_stage_ray_slack"], 1e-3)
+        self.assertTrue(summary["pose_stage_summaries"][2]["accepted"])
+
     def test_guarded_two_step_bundle_adjustment_rejects_on_hard_geometry_guard(self):
         points = np.array(
             [
@@ -779,6 +935,152 @@ def test_guarded_two_step_bundle_adjustment_rejects_on_soft_geometry_guard(self)
         )
         np.testing.assert_allclose(final_cals[3].get_pos(), pose_cals[3].get_pos())
 
+    def test_guarded_two_step_bundle_adjustment_rejects_on_hard_correspondence_guard(self):
+        points = np.array(
+            [
+                [0.0, 0.0, 0.0],
+                [20.0, 0.0, 0.0],
+                [40.0, 0.0, 0.0],
+                [60.0, 0.0, 0.0],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.zeros((len(points), 4, 2), dtype=float)
+        pose_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        for cal in pose_cals:
+            cal.set_pos(np.zeros(3, dtype=float))
+            cal.set_angles(np.zeros(3, dtype=float))
+        start_cals = [self.clone_calibration(cal) for cal in pose_cals]
+        bad_intrinsic_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        for cal in bad_intrinsic_cals:
+            cal.set_pos(np.zeros(3, dtype=float))
+            cal.set_angles(np.zeros(3, dtype=float))
+        bad_intrinsic_cals[2].set_pos(
+            bad_intrinsic_cals[2].get_pos() + np.array([25.0, 0.0, 0.0])
+        )
+
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+        target_sets = [
+            np.asarray([[point[0], point[1]] for point in points], dtype=float)
+            for _ in range(4)
+        ]
+
+        with patch(
+            "openptv_python.orientation.multi_camera_bundle_adjustment",
+            side_effect=[
+                (pose_cals, points.copy(), {"success": True}),
+                (bad_intrinsic_cals, points.copy(), {"success": True}),
+            ],
+        ), patch(
+            "openptv_python.orientation.reprojection_rms",
+            side_effect=[10.0, 5.0, 4.0],
+        ), patch(
+            "openptv_python.orientation.mean_ray_convergence",
+            side_effect=[3.0, 2.0, 1.0],
+        ), patch(
+            "openptv_python.orientation.image_coordinates",
+            side_effect=lambda pts, cal, _mm: pts[:, :2] + cal.get_pos()[:2],
+        ), patch(
+            "openptv_python.orientation.arr_metric_to_pixel",
+            side_effect=lambda coords, _cpar: coords,
+        ):
+            final_cals, _, summary = guarded_two_step_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                intrinsics,
+                point_init=points,
+                fixed_camera_indices=[0, 1],
+                correspondence_original_ids=np.tile(np.arange(len(points))[:, None], (1, 4)),
+                correspondence_point_frame_indices=np.zeros(len(points), dtype=int),
+                correspondence_frame_target_pixels=[target_sets],
+                correspondence_guard_mode="hard",
+                correspondence_guard_threshold=0.2,
+            )
+
+        self.assertEqual(summary["accepted_stage"], "pose")
+        self.assertTrue(summary["pose_correspondence_ok"])
+        self.assertFalse(summary["intrinsic_correspondence_ok"])
+        self.assertGreater(summary["intrinsic_correspondence_rate"], 0.2)
+        np.testing.assert_allclose(final_cals[2].get_pos(), pose_cals[2].get_pos())
+
+    def test_guarded_two_step_bundle_adjustment_rejects_on_soft_correspondence_guard(self):
+        points = np.array(
+            [
+                [0.0, 0.0, 0.0],
+                [20.0, 0.0, 0.0],
+                [40.0, 0.0, 0.0],
+                [60.0, 0.0, 0.0],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.zeros((len(points), 4, 2), dtype=float)
+        pose_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        for cal in pose_cals:
+            cal.set_pos(np.zeros(3, dtype=float))
+            cal.set_angles(np.zeros(3, dtype=float))
+        start_cals = [self.clone_calibration(cal) for cal in pose_cals]
+        bad_intrinsic_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        for cal in bad_intrinsic_cals:
+            cal.set_pos(np.zeros(3, dtype=float))
+            cal.set_angles(np.zeros(3, dtype=float))
+        bad_intrinsic_cals[3].set_pos(
+            bad_intrinsic_cals[3].get_pos() + np.array([25.0, 0.0, 0.0])
+        )
+
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+        target_sets = [
+            np.asarray([[point[0], point[1]] for point in points], dtype=float)
+            for _ in range(4)
+        ]
+
+        with patch(
+            "openptv_python.orientation.multi_camera_bundle_adjustment",
+            side_effect=[
+                (pose_cals, points.copy(), {"success": True}),
+                (bad_intrinsic_cals, points.copy(), {"success": True}),
+            ],
+        ), patch(
+            "openptv_python.orientation.reprojection_rms",
+            side_effect=[10.0, 5.0, 4.0],
+        ), patch(
+            "openptv_python.orientation.mean_ray_convergence",
+            side_effect=[3.0, 2.0, 1.0],
+        ), patch(
+            "openptv_python.orientation.image_coordinates",
+            side_effect=lambda pts, cal, _mm: pts[:, :2] + cal.get_pos()[:2],
+        ), patch(
+            "openptv_python.orientation.arr_metric_to_pixel",
+            side_effect=lambda coords, _cpar: coords,
+        ):
+            final_cals, _, summary = guarded_two_step_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                intrinsics,
+                point_init=points,
+                fixed_camera_indices=[0, 1],
+                correspondence_original_ids=np.tile(np.arange(len(points))[:, None], (1, 4)),
+                correspondence_point_frame_indices=np.zeros(len(points), dtype=int),
+                correspondence_frame_target_pixels=[target_sets],
+                correspondence_guard_mode="soft",
+                correspondence_guard_reference_rate=0.0,
+            )
+
+        self.assertEqual(summary["accepted_stage"], "pose")
+        self.assertTrue(summary["pose_correspondence_ok"])
+        self.assertFalse(summary["intrinsic_correspondence_ok"])
+        self.assertGreater(summary["intrinsic_correspondence_rate"], 0.0)
+        np.testing.assert_allclose(final_cals[3].get_pos(), pose_cals[3].get_pos())
+
     def test_cavity_intrinsics_only_improves_from_intrinsic_perturbation(self):
         cavity_dir = Path("tests/testing_fodder/test_cavity")
         control = ControlPar(4).from_file(cavity_dir / "parameters/ptv.par")
diff --git a/tests/test_demo_bundle_adjustment.py b/tests/test_demo_bundle_adjustment.py
index dcdb78e..cd21f0e 100644
--- a/tests/test_demo_bundle_adjustment.py
+++ b/tests/test_demo_bundle_adjustment.py
@@ -13,6 +13,7 @@
     format_quadruplet_sensitivity,
     load_calibrations,
     load_reference_geometry_points,
+    normalize_staged_release_order,
     perturb_calibrations,
     summarize_epipolar_consistency,
     summarize_fixed_camera_diagnostics,
@@ -39,6 +40,7 @@ def test_default_experiments_adds_known_point_presets(self):
         known_points = {0: np.array([0.0, 0.0, 0.0]), 5: np.array([1.0, 2.0, 3.0])}
 
         experiments = default_experiments(
+            num_cams=4,
             known_points=known_points,
             known_point_sigmas=0.25,
         )
@@ -47,6 +49,8 @@ def test_default_experiments_adds_known_point_presets(self):
         self.assertIn("intrinsics_only", names)
         self.assertIn("pose_trf_known_points", names)
         self.assertIn("guarded_two_step_known_points", names)
+        self.assertIn("guarded_stagewise_release", names)
+        self.assertIn("guarded_stagewise_release_known_points", names)
 
         intrinsics_only = next(
             spec for spec in experiments if spec.name == "intrinsics_only"
@@ -69,26 +73,72 @@ def test_default_experiments_adds_known_point_presets(self):
             for spec in experiments
             if spec.name == "guarded_two_step_known_points"
         )
+        staged_spec = next(
+            spec
+            for spec in experiments
+            if spec.name == "guarded_stagewise_release"
+        )
+        staged_known_spec = next(
+            spec
+            for spec in experiments
+            if spec.name == "guarded_stagewise_release_known_points"
+        )
         self.assertIs(pose_spec.ba_kwargs["known_points"], known_points)
         self.assertEqual(pose_spec.ba_kwargs["known_point_sigmas"], 0.25)
         self.assertIs(guarded_spec.ba_kwargs["known_points"], known_points)
         self.assertEqual(guarded_spec.ba_kwargs["known_point_sigmas"], 0.25)
+        self.assertEqual(staged_spec.ba_kwargs["fixed_camera_indices"], [1, 2, 3])
+        self.assertEqual(staged_spec.ba_kwargs["pose_release_camera_order"], [0, 1, 2, 3])
+        self.assertEqual(staged_spec.ba_kwargs["pose_stage_ray_slack"], 0.0)
+        self.assertIs(staged_known_spec.ba_kwargs["known_points"], known_points)
+        self.assertEqual(staged_known_spec.ba_kwargs["known_point_sigmas"], 0.25)
         self.assertEqual(guarded_spec.ba_kwargs["geometry_guard_mode"], "off")
         self.assertIsNone(guarded_spec.ba_kwargs["geometry_guard_threshold"])
+        self.assertEqual(guarded_spec.ba_kwargs["correspondence_guard_mode"], "off")
+        self.assertIsNone(guarded_spec.ba_kwargs["correspondence_guard_threshold"])
+        self.assertIsNone(guarded_spec.ba_kwargs["correspondence_guard_reference_rate"])
 
     def test_default_experiments_accepts_geometry_guard_configuration(self):
         experiments = default_experiments(
+            num_cams=4,
             perturbation_scale=1.0,
+            staged_release_order=[2, 0, 1, 3],
+            pose_stage_ray_slack=0.002,
             geometry_guard_mode="hard",
             geometry_guard_threshold=2.5,
+            correspondence_guard_mode="hard",
+            correspondence_guard_threshold=0.18,
+            correspondence_guard_reference_rate=0.15625,
         )
 
         guarded_spec = next(
             spec for spec in experiments if spec.name == "guarded_two_step"
         )
+        staged_spec = next(
+            spec for spec in experiments if spec.name == "guarded_stagewise_release"
+        )
 
         self.assertEqual(guarded_spec.ba_kwargs["geometry_guard_mode"], "hard")
         self.assertEqual(guarded_spec.ba_kwargs["geometry_guard_threshold"], 2.5)
+        self.assertEqual(guarded_spec.ba_kwargs["correspondence_guard_mode"], "hard")
+        self.assertEqual(guarded_spec.ba_kwargs["correspondence_guard_threshold"], 0.18)
+        self.assertEqual(
+            guarded_spec.ba_kwargs["correspondence_guard_reference_rate"],
+            0.15625,
+        )
+        self.assertEqual(staged_spec.ba_kwargs["pose_release_camera_order"], [2, 0, 1, 3])
+        self.assertEqual(staged_spec.ba_kwargs["fixed_camera_indices"], [0, 1, 3])
+        self.assertEqual(staged_spec.ba_kwargs["pose_stage_ray_slack"], 0.002)
+
+    def test_normalize_staged_release_order_validates_camera_permutation(self):
+        self.assertEqual(normalize_staged_release_order(None, 4), [0, 1, 2, 3])
+        self.assertEqual(normalize_staged_release_order([2, 0, 1, 3], 4), [2, 0, 1, 3])
+
+        with self.assertRaises(ValueError):
+            normalize_staged_release_order([0, 1, 1, 3], 4)
+
+        with self.assertRaises(ValueError):
+            normalize_staged_release_order([0, 1, 2], 4)
 
     def test_pose_demo_keeps_fixed_cameras_on_reference_geometry(self):
         cavity_dir = Path("tests/testing_fodder/test_cavity")

From 9a34c9429a9d50a53c675afb5d415794d88ea0f5 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 01:52:33 +0200
Subject: [PATCH 10/27] updated bundle adjustment and docs

/memories/repo/openptv-python-2026-03-07-handoff.md.
---
 Makefile                                      |   7 +-
 docs/bundle_adjustment.md                     |  11 +-
 openptv_python/demo_bundle_adjustment.py      | 171 +++++++++---
 .../generate_synthetic_cavity_case.py         |  85 ++++--
 openptv_python/orientation.py                 |  28 +-
 tests/test_bundle_adjustment.py               | 259 ++++++++++--------
 tests/test_demo_bundle_adjustment.py          |  28 +-
 tests/test_synthetic_cavity_case.py           |  20 +-
 8 files changed, 405 insertions(+), 204 deletions(-)

diff --git a/Makefile b/Makefile
index 895ed00..7cee62b 100644
--- a/Makefile
+++ b/Makefile
@@ -2,17 +2,18 @@ PROJECT := openptv_python
 CONDA := conda
 CONDAFLAGS :=
 COV_REPORT := html
+PYTHON ?= .venv/bin/python
 
 default: qa unit-tests type-check
 
 qa:
-	pre-commit run --all-files
+	$(PYTHON) -m pre_commit run --all-files
 
 unit-tests:
-	python -m pytest -vv --cov=. --cov-report=$(COV_REPORT) --doctest-glob="*.md" --doctest-glob="*.rst"
+	$(PYTHON) -m pytest -vv --cov=. --cov-report=$(COV_REPORT) --doctest-glob="*.md" --doctest-glob="*.rst"
 
 type-check:
-	python -m mypy .
+	$(PYTHON) -m mypy .
 
 conda-env-update:
 	$(CONDA) install -y -c conda-forge conda-merge
diff --git a/docs/bundle_adjustment.md b/docs/bundle_adjustment.md
index 59919d5..779f30e 100644
--- a/docs/bundle_adjustment.md
+++ b/docs/bundle_adjustment.md
@@ -50,11 +50,12 @@ where $\\theta_j$ denotes the active calibration parameters for camera $j$.
 The solver minimizes a robust or linear least-squares objective
 
 $$
-\min_{\Theta, X}
-\sum_{i,j} \rho\left(\left\|W r_{ij}\right\|^2\right)
-+ \sum_k \left(\frac{p_k - p_{k,0}}{\sigma_k}\right)^2
-+ \sum_{m \in \mathcal{M}} \left\|\frac{X_m - X_m^*}{\sigma^{(X)}_m}\right\|^2.
-$$
+\\min\_{\\Theta, X}
+\\sum\_{i,j} \\rho\\left(\\left|W r\_{ij}\\right|^2\\right)
+
+- \\sum_k \\left(\\frac{p_k - p\_{k,0}}{\\sigma_k}\\right)^2
+- \\sum\_{m \\in \\mathcal{M}} \\left|\\frac{X_m - X_m^\*}{\\sigma^{(X)}\_m}\\right|^2.
+  $$
 
 with:
 
diff --git a/openptv_python/demo_bundle_adjustment.py b/openptv_python/demo_bundle_adjustment.py
index 6aca494..4b44b0a 100644
--- a/openptv_python/demo_bundle_adjustment.py
+++ b/openptv_python/demo_bundle_adjustment.py
@@ -22,7 +22,6 @@
     guarded_two_step_bundle_adjustment,
     initialize_bundle_adjustment_points,
     mean_ray_convergence,
-    metric_observations_from_pixels,
     multi_camera_bundle_adjustment,
     reprojection_rms,
 )
@@ -171,7 +170,9 @@ def perturb_calibrations(cals: List[Calibration], scale: float) -> List[Calibrat
     return perturbed
 
 
-def perturb_intrinsic_parameters(cals: List[Calibration], scale: float) -> List[Calibration]:
+def perturb_intrinsic_parameters(
+    cals: List[Calibration], scale: float
+) -> List[Calibration]:
     """Apply deterministic distortion perturbations while keeping poses fixed."""
     perturbed = [clone_calibration(cal) for cal in cals]
     deltas = [
@@ -216,7 +217,9 @@ def build_experiment_start_calibrations(
     for camera_index in fixed_camera_indices:
         if camera_index < 0 or camera_index >= len(working_cals):
             continue
-        working_cals[camera_index].set_pos(reference_cals[camera_index].get_pos().copy())
+        working_cals[camera_index].set_pos(
+            reference_cals[camera_index].get_pos().copy()
+        )
         working_cals[camera_index].set_angles(
             reference_cals[camera_index].get_angles().copy()
         )
@@ -295,7 +298,10 @@ def format_projection_drift(summaries: Sequence[ProjectionDriftSummary] | None)
     lines = ["camera  mean_px  p95_px  max_px", "------  -------  ------  ------"]
     for item in summaries:
         lines.append(
-            f"{item.camera_index:>6}  {item.mean_distance:>7.3f}  {item.p95_distance:>6.3f}  {item.max_distance:>6.3f}"
+            (
+                f"{item.camera_index:>6}  {item.mean_distance:>7.3f}  "
+                f"{item.p95_distance:>6.3f}  {item.max_distance:>6.3f}"
+            )
         )
     return "\n".join(lines)
 
@@ -321,7 +327,10 @@ def should_block_export_on_geometry(
     if drift_max is None or drift_max <= threshold_px:
         return False, ""
 
-    return True, f"geometry_export_blocked=max_drift={drift_max:.3f}px>{threshold_px:.3f}px"
+    return (
+        True,
+        f"geometry_export_blocked=max_drift={drift_max:.3f}px>{threshold_px:.3f}px",
+    )
 
 
 def discover_num_cams(cal_dir: Path) -> int:
@@ -423,7 +432,10 @@ def summarize_correspondence_replacements(
             int(tracking_data.point_frame_indices[point_index])
         ]
         for camera_index in range(len(cals)):
-            deltas = frame_targets[camera_index] - projected_pixels[point_index, camera_index]
+            deltas = (
+                frame_targets[camera_index]
+                - projected_pixels[point_index, camera_index]
+            )
             squared_distances = np.sum(deltas * deltas, axis=1)
             nearest_index = int(np.argmin(squared_distances))
             replacement_ids[point_index, camera_index] = nearest_index
@@ -497,7 +509,12 @@ def load_case_tracking_data(
             reference_points.append(path_buf[point_num].x.copy())
 
         frame_target_pixels.append(
-            [np.asarray([[target.x, target.y] for target in cam_targets], dtype=float) for cam_targets in targets]
+            [
+                np.asarray(
+                    [[target.x, target.y] for target in cam_targets], dtype=float
+                )
+                for cam_targets in targets
+            ]
         )
 
     if not original_target_ids:
@@ -731,7 +748,12 @@ def format_epipolar_diagnostics(
             f"{item.p95_distance:.6f}",
             f"{item.max_distance:.6f}",
         ]
-        row.extend([f"{100.0 * item.acceptance_rates[threshold]:.1f}%" for threshold in thresholds])
+        row.extend(
+            [
+                f"{100.0 * item.acceptance_rates[threshold]:.1f}%"
+                for threshold in thresholds
+            ]
+        )
         data.append(row)
 
     widths = [len(header) for header in headers]
@@ -740,7 +762,9 @@ def format_epipolar_diagnostics(
             widths[index] = max(widths[index], len(value))
 
     def render_row(values: List[str]) -> str:
-        return "  ".join(value.ljust(widths[index]) for index, value in enumerate(values))
+        return "  ".join(
+            value.ljust(widths[index]) for index, value in enumerate(values)
+        )
 
     separator = "  ".join("-" * width for width in widths)
     lines = [render_row(headers), separator]
@@ -760,7 +784,9 @@ def summarize_quadruplet_sensitivity(
     full_mask = np.all(np.isfinite(observed_pixels), axis=(1, 2))
     full_indices = np.flatnonzero(full_mask)
     if full_indices.size == 0:
-        raise ValueError("No fully observed quadruplets available for sensitivity analysis")
+        raise ValueError(
+            "No fully observed quadruplets available for sensitivity analysis"
+        )
 
     full_points, full_ray_convergence = initialize_bundle_adjustment_points(
         observed_pixels[full_indices],
@@ -774,7 +800,9 @@ def summarize_quadruplet_sensitivity(
         subset_points = []
         subset_rays = []
         for omitted_camera in range(observed_pixels.shape[1]):
-            keep = [cam for cam in range(observed_pixels.shape[1]) if cam != omitted_camera]
+            keep = [
+                cam for cam in range(observed_pixels.shape[1]) if cam != omitted_camera
+            ]
             subset_observed = observed_pixels[point_index : point_index + 1, keep, :]
             subset_cals = [cals[cam] for cam in keep]
             subset_point, subset_ray = initialize_bundle_adjustment_points(
@@ -800,7 +828,9 @@ def summarize_quadruplet_sensitivity(
         max_spread=float(np.max(spreads)),
         mean_full_ray_convergence=float(np.mean(full_ray_convergence)),
         mean_leave_one_out_ray_convergence=float(np.mean(leave_one_out_rays)),
-        worst_point_indices=[int(full_indices[index]) for index in worst_order.tolist()],
+        worst_point_indices=[
+            int(full_indices[index]) for index in worst_order.tolist()
+        ],
     )
 
 
@@ -842,7 +872,9 @@ def format_quadruplet_sensitivity(
             widths[index] = max(widths[index], len(value))
 
     def render_row(values: List[str]) -> str:
-        return "  ".join(value.ljust(widths[index]) for index, value in enumerate(values))
+        return "  ".join(
+            value.ljust(widths[index]) for index, value in enumerate(values)
+        )
 
     separator = "  ".join("-" * width for width in widths)
     lines = [render_row(list(headers)), separator]
@@ -857,11 +889,14 @@ def summarize_fixed_camera_diagnostics(
     diagnostics = []
     for result in results:
         if len(result.fixed_camera_indices) != 2:
-            raise ValueError("Fixed-camera diagnostics require exactly two fixed cameras")
+            raise ValueError(
+                "Fixed-camera diagnostics require exactly two fixed cameras"
+            )
 
         fixed_indices = set(result.fixed_camera_indices)
         fixed_position_shifts = [
-            result.camera_position_shifts[index] for index in result.fixed_camera_indices
+            result.camera_position_shifts[index]
+            for index in result.fixed_camera_indices
         ]
         fixed_angle_shifts = [
             result.camera_angle_shifts[index] for index in result.fixed_camera_indices
@@ -940,7 +975,9 @@ def default_experiments(
     """Return a set of representative BA configurations."""
     staged_order = normalize_staged_release_order(staged_release_order, num_cams)
     staged_fixed = [
-        camera_index for camera_index in range(num_cams) if camera_index != staged_order[0]
+        camera_index
+        for camera_index in range(num_cams)
+        if camera_index != staged_order[0]
     ]
     pose_priors = {
         "x0": 0.5,
@@ -1040,7 +1077,10 @@ def default_experiments(
         ),
         ExperimentSpec(
             name="intrinsics_only",
-            description="Intrinsics-only BA from fixed reference poses with tightly bounded distortion updates",
+            description=(
+                "Intrinsics-only BA from fixed reference poses with tightly "
+                "bounded distortion updates"
+            ),
             mode="multi",
             ba_kwargs={
                 "orient_par": intrinsics_only,
@@ -1554,7 +1594,9 @@ def format_fixed_camera_diagnostics(
             widths[index] = max(widths[index], len(value))
 
     def render_row(values: List[str]) -> str:
-        return "  ".join(value.ljust(widths[index]) for index, value in enumerate(values))
+        return "  ".join(
+            value.ljust(widths[index]) for index, value in enumerate(values)
+        )
 
     separator = "  ".join("-" * width for width in widths)
     lines = [render_row(list(headers)), separator]
@@ -1599,7 +1641,9 @@ def summarize_anchor_participation(
             widths[index] = max(widths[index], len(value))
 
     def render_row(values: List[str]) -> str:
-        return "  ".join(value.ljust(widths[index]) for index, value in enumerate(values))
+        return "  ".join(
+            value.ljust(widths[index]) for index, value in enumerate(values)
+        )
 
     separator = "  ".join("-" * width for width in widths)
     lines = [render_row(list(headers)), separator]
@@ -1735,7 +1779,10 @@ def parse_args(argv: Iterable[str] | None = None) -> argparse.Namespace:
     parser.add_argument(
         "--diagnose-quadruplets",
         action="store_true",
-        help="Compare leave-one-camera-out quadruplet stability before and after the selected diagnostic experiment.",
+        help=(
+            "Compare leave-one-camera-out quadruplet stability before and after "
+            "the selected diagnostic experiment."
+        ),
     )
     parser.add_argument(
         "--epipolar-curve-points",
@@ -1748,7 +1795,10 @@ def parse_args(argv: Iterable[str] | None = None) -> argparse.Namespace:
         type=str,
         choices=("auto", "off", "soft", "hard"),
         default="auto",
-        help="Guarded-BA geometry acceptance mode. 'auto' uses 'hard' when cal_ori.par exposes known 3D target points.",
+        help=(
+            "Guarded-BA geometry acceptance mode. 'auto' uses 'hard' when "
+            "cal_ori.par exposes known 3D target points."
+        ),
     )
     parser.add_argument(
         "--geometry-guard-threshold",
@@ -1760,38 +1810,60 @@ def parse_args(argv: Iterable[str] | None = None) -> argparse.Namespace:
         "--geometry-export-threshold",
         type=float,
         default=None,
-        help="Do not write output case folders whose final calibration-body drift exceeds this many pixels; 0 disables export blocking.",
+        help=(
+            "Do not write output case folders whose final calibration-body "
+            "drift exceeds this many pixels; 0 disables export blocking."
+        ),
     )
     parser.add_argument(
         "--correspondence-guard-mode",
         type=str,
         choices=("auto", "off", "soft", "hard"),
         default="auto",
-        help="Guarded-BA correspondence acceptance mode. 'auto' uses a hard replacement-rate guard when original quadruplet identities are available.",
+        help=(
+            "Guarded-BA correspondence acceptance mode. 'auto' uses a hard "
+            "replacement-rate guard when original quadruplet identities are "
+            "available."
+        ),
     )
     parser.add_argument(
         "--correspondence-guard-threshold",
         type=float,
         default=None,
-        help="Maximum allowed quadruplet replacement rate for hard correspondence guards. If omitted, the demo derives a case-specific threshold from the trusted reference calibration.",
+        help=(
+            "Maximum allowed quadruplet replacement rate for hard "
+            "correspondence guards. If omitted, the demo derives a case-"
+            "specific threshold from the trusted reference calibration."
+        ),
     )
     parser.add_argument(
         "--correspondence-export-threshold",
         type=float,
         default=None,
-        help="Do not write output case folders whose final quadruplet replacement rate exceeds this threshold; 0 disables correspondence-based export blocking.",
+        help=(
+            "Do not write output case folders whose final quadruplet "
+            "replacement rate exceeds this threshold; 0 disables "
+            "correspondence-based export blocking."
+        ),
     )
     parser.add_argument(
         "--staged-release-order",
         type=str,
         default=None,
-        help="Comma-separated 1-based camera release order for staged guarded presets, for example '1,2,3,4'.",
+        help=(
+            "Comma-separated 1-based camera release order for staged guarded "
+            "presets, for example '1,2,3,4'."
+        ),
     )
     parser.add_argument(
         "--staged-ray-slack",
         type=float,
         default=0.0,
-        help="Allow staged guarded pose-release steps to worsen mean ray convergence by up to this amount relative to the last accepted stage.",
+        help=(
+            "Allow staged guarded pose-release steps to worsen mean ray "
+            "convergence by up to this amount relative to the last accepted "
+            "stage."
+        ),
     )
     return parser.parse_args(list(argv) if argv is not None else None)
 
@@ -1836,7 +1908,10 @@ def main(argv: Iterable[str] | None = None) -> int:
         correspondence_guard_mode = "hard" if tracking_data is not None else "off"
     else:
         correspondence_guard_mode = args.correspondence_guard_mode
-    if tracking_data is not None and tracking_data.reference_replacement_rate is not None:
+    if (
+        tracking_data is not None
+        and tracking_data.reference_replacement_rate is not None
+    ):
         auto_correspondence_threshold = min(
             0.25,
             max(0.05, tracking_data.reference_replacement_rate + 0.02),
@@ -1894,15 +1969,25 @@ def main(argv: Iterable[str] | None = None) -> int:
         print("Known-point anchors: disabled")
     if reference_geometry_points is not None:
         print(
-            f"Geometry guard: mode={geometry_guard_mode}, guard_threshold={args.geometry_guard_threshold:.3f}px, export_threshold={geometry_export_threshold:.3f}px"
+            "Geometry guard: "
+            f"mode={geometry_guard_mode}, "
+            f"guard_threshold={args.geometry_guard_threshold:.3f}px, "
+            f"export_threshold={geometry_export_threshold:.3f}px"
         )
     else:
-        print("Geometry guard: unavailable for this case (no known 3D target file found)")
-    if tracking_data is not None and tracking_data.reference_replacement_rate is not None:
+        print(
+            "Geometry guard: unavailable for this case (no known 3D target file found)"
+        )
+    if (
+        tracking_data is not None
+        and tracking_data.reference_replacement_rate is not None
+    ):
         print(
             "Correspondence guard: "
-            f"mode={correspondence_guard_mode}, reference_rate={tracking_data.reference_replacement_rate:.3f}, "
-            f"guard_threshold={correspondence_guard_threshold:.3f}, export_threshold={correspondence_export_threshold:.3f}"
+            f"mode={correspondence_guard_mode}, "
+            f"reference_rate={tracking_data.reference_replacement_rate:.3f}, "
+            f"guard_threshold={correspondence_guard_threshold:.3f}, "
+            f"export_threshold={correspondence_export_threshold:.3f}"
         )
     else:
         print("Correspondence guard: unavailable for this case")
@@ -1945,9 +2030,7 @@ def main(argv: Iterable[str] | None = None) -> int:
             correspondence_export_threshold=correspondence_export_threshold,
             source_case_dir=case_dir,
         )
-        print(
-            f"Fixed-pair diagnostics for {spec.name}: {spec.description}"
-        )
+        print(f"Fixed-pair diagnostics for {spec.name}: {spec.description}")
         print(format_fixed_camera_diagnostics(diagnostics))
         print()
         print("Anchor participation summary")
@@ -1976,13 +2059,15 @@ def main(argv: Iterable[str] | None = None) -> int:
             output_dir=None,
         )
         if diagnostic_result.refined_cals is None:
-            raise ValueError("Diagnostic experiment did not return refined calibrations")
+            raise ValueError(
+                "Diagnostic experiment did not return refined calibrations"
+            )
 
+        print(f"Diagnostics for {spec.name}: {spec.description}")
         print(
-            f"Diagnostics for {spec.name}: {spec.description}"
-        )
-        print(
-            f"Result RMS: {diagnostic_result.final_rms:.6f} px, ray: {diagnostic_result.final_ray_convergence:.6f}, notes: {diagnostic_result.notes}"
+            f"Result RMS: {diagnostic_result.final_rms:.6f} px, "
+            f"ray: {diagnostic_result.final_ray_convergence:.6f}, "
+            f"notes: {diagnostic_result.notes}"
         )
         print()
 
@@ -2022,7 +2107,9 @@ def main(argv: Iterable[str] | None = None) -> int:
                 control,
             )
             print("Quadruplet leave-one-camera-out sensitivity")
-            print(format_quadruplet_sensitivity(baseline_quadruplets, final_quadruplets))
+            print(
+                format_quadruplet_sensitivity(baseline_quadruplets, final_quadruplets)
+            )
 
         return 0
 
diff --git a/openptv_python/generate_synthetic_cavity_case.py b/openptv_python/generate_synthetic_cavity_case.py
index 585d520..f7cab0a 100644
--- a/openptv_python/generate_synthetic_cavity_case.py
+++ b/openptv_python/generate_synthetic_cavity_case.py
@@ -21,7 +21,13 @@
 )
 from .parameters import ControlPar, OrientPar, VolumePar, read_volume_par
 from .sortgrid import read_sortgrid_par
-from .tracking_frame_buf import Pathinfo, Target, n_tupel_dtype, write_path_frame, write_targets
+from .tracking_frame_buf import (
+    Pathinfo,
+    Target,
+    n_tupel_dtype,
+    write_path_frame,
+    write_targets,
+)
 from .trafo import arr_metric_to_pixel
 
 DEFAULT_SOURCE_CASE = Path("tests/testing_fodder/test_cavity")
@@ -68,7 +74,9 @@ def make_target(x: float, y: float, pnr: int) -> Target:
     )
 
 
-def camera_points_in_bounds(pixel_points: np.ndarray, cpar: ControlPar, margin: float = 8.0) -> np.ndarray:
+def camera_points_in_bounds(
+    pixel_points: np.ndarray, cpar: ControlPar, margin: float = 8.0
+) -> np.ndarray:
     """Return a mask of points lying safely inside the sensor bounds."""
     return (
         (pixel_points[:, 0] >= margin)
@@ -91,7 +99,9 @@ def z_bounds_at_x(x_coord: float, vpar: VolumePar) -> tuple[float, float]:
     return float(z_min), float(z_max)
 
 
-def project_pixels(points_3d: np.ndarray, cals: Sequence[Calibration], cpar: ControlPar) -> np.ndarray:
+def project_pixels(
+    points_3d: np.ndarray, cals: Sequence[Calibration], cpar: ControlPar
+) -> np.ndarray:
     """Project 3D points into all cameras in pixel coordinates."""
     observed = np.empty((points_3d.shape[0], len(cals), 2), dtype=np.float64)
     for cam_index, cal in enumerate(cals):
@@ -119,7 +129,9 @@ def select_visible_points(
     return visible_points[:count]
 
 
-def build_calibration_body(vpar: VolumePar, cals: Sequence[Calibration], cpar: ControlPar) -> np.ndarray:
+def build_calibration_body(
+    vpar: VolumePar, cals: Sequence[Calibration], cpar: ControlPar
+) -> np.ndarray:
     """Build a structured 3D calibration body visible in all cameras."""
     xs = np.linspace(vpar.x_lay[0] + 4.0, vpar.x_lay[1] - 4.0, 8)
     ys = np.linspace(-16.0, 16.0, 6)
@@ -152,7 +164,10 @@ def generate_particle_cloud(
         z_coord = float(rng.uniform(z_min + 2.0, z_max - 2.0))
         point = np.asarray([[x_coord, y_coord, z_coord]], dtype=np.float64)
         projected = project_pixels(point, cals, cpar)[0]
-        if all(camera_points_in_bounds(projected[None, cam_index, :], cpar)[0] for cam_index in range(len(cals))):
+        if all(
+            camera_points_in_bounds(projected[None, cam_index, :], cpar)[0]
+            for cam_index in range(len(cals))
+        ):
             accepted.append(point[0])
     return np.asarray(accepted, dtype=np.float64)
 
@@ -165,7 +180,12 @@ def shuffled_targets_from_pixels(
     """Shuffle projected pixels into a target list and return point-to-target indices."""
     noisy_pixels = pixel_points + rng.normal(0.0, noise_sigma, size=pixel_points.shape)
     order = rng.permutation(pixel_points.shape[0])
-    targets = [make_target(noisy_pixels[target_index, 0], noisy_pixels[target_index, 1], list_index) for list_index, target_index in enumerate(order)]
+    targets = [
+        make_target(
+            noisy_pixels[target_index, 0], noisy_pixels[target_index, 1], list_index
+        )
+        for list_index, target_index in enumerate(order)
+    ]
     point_to_target = np.empty(pixel_points.shape[0], dtype=np.int32)
     for list_index, target_index in enumerate(order):
         point_to_target[target_index] = list_index
@@ -174,11 +194,16 @@ def shuffled_targets_from_pixels(
 
 def write_calibration_body_points(points: np.ndarray, output_file: Path) -> None:
     """Write a calblock-compatible calibration body file."""
-    lines = [f"{index + 1:11d}{point[0]:11.3f}{point[1]:11.3f}{point[2]:11.3f}" for index, point in enumerate(points)]
+    lines = [
+        f"{index + 1:11d}{point[0]:11.3f}{point[1]:11.3f}{point[2]:11.3f}"
+        for index, point in enumerate(points)
+    ]
     output_file.write_text("\n".join(lines) + "\n", encoding="utf-8")
 
 
-def perturb_calibration_for_recovery(cal: Calibration, camera_index: int) -> Calibration:
+def perturb_calibration_for_recovery(
+    cal: Calibration, camera_index: int
+) -> Calibration:
     """Create a deterministic seed calibration for full_calibration recovery."""
     trial = clone_calibration(cal)
     position_deltas = [
@@ -229,7 +254,9 @@ def recover_calibrations_from_body(
     seed_indices = select_external_seed_subset(ref_points)
 
     for camera_index, truth_cal in enumerate(truth_cals, start=1):
-        projected = arr_metric_to_pixel(image_coordinates(ref_points, truth_cal, cpar.mm), cpar)
+        projected = arr_metric_to_pixel(
+            image_coordinates(ref_points, truth_cal, cpar.mm), cpar
+        )
         targets, _ = shuffled_targets_from_pixels(projected, rng, noise_sigma=0.0)
         write_targets(
             targets,
@@ -245,7 +272,9 @@ def recover_calibrations_from_body(
             projected[seed_indices],
             cpar,
         )
-        sorted_targets = match_detection_to_ref(seed, ref_points, targets, cpar, sortgrid_eps)
+        sorted_targets = match_detection_to_ref(
+            seed, ref_points, targets, cpar, sortgrid_eps
+        )
         full_calibration(
             seed,
             ref_points,
@@ -266,8 +295,12 @@ def recover_calibrations_from_body(
             truth_cal_dir / f"cam{camera_index}.tif.addpar",
         )
 
-        position_errors.append(float(np.linalg.norm(seed.get_pos() - truth_cal.get_pos())))
-        angle_errors.append(float(np.linalg.norm(seed.get_angles() - truth_cal.get_angles())))
+        position_errors.append(
+            float(np.linalg.norm(seed.get_pos() - truth_cal.get_pos()))
+        )
+        angle_errors.append(
+            float(np.linalg.norm(seed.get_angles() - truth_cal.get_angles()))
+        )
 
     return recovered, position_errors, angle_errors
 
@@ -283,7 +316,9 @@ def build_frame_targets_and_paths(
     per_camera_targets = []
     per_camera_mapping = []
     for cam_index in range(len(cals)):
-        targets, point_to_target = shuffled_targets_from_pixels(projected[:, cam_index, :], rng, noise_sigma=0.08)
+        targets, point_to_target = shuffled_targets_from_pixels(
+            projected[:, cam_index, :], rng, noise_sigma=0.08
+        )
         per_camera_targets.append(targets)
         per_camera_mapping.append(point_to_target)
 
@@ -291,15 +326,24 @@ def build_frame_targets_and_paths(
     for point_index in range(frame_points.shape[0]):
         for cam_index in range(len(cals)):
             target_index = per_camera_mapping[cam_index][point_index]
-            observed_pixels[point_index, cam_index, 0] = per_camera_targets[cam_index][target_index].x
-            observed_pixels[point_index, cam_index, 1] = per_camera_targets[cam_index][target_index].y
-
-    initial_points, _ = initialize_bundle_adjustment_points(observed_pixels, list(cals), cpar)
+            observed_pixels[point_index, cam_index, 0] = per_camera_targets[cam_index][
+                target_index
+            ].x
+            observed_pixels[point_index, cam_index, 1] = per_camera_targets[cam_index][
+                target_index
+            ].y
+
+    initial_points, _ = initialize_bundle_adjustment_points(
+        observed_pixels, list(cals), cpar
+    )
     cor_buf = np.recarray((frame_points.shape[0],), dtype=n_tupel_dtype)
     path_buf = [Pathinfo() for _ in range(frame_points.shape[0])]
     for point_index in range(frame_points.shape[0]):
         cor_buf[point_index].p = np.array(
-            [per_camera_mapping[cam_index][point_index] for cam_index in range(len(cals))],
+            [
+                per_camera_mapping[cam_index][point_index]
+                for cam_index in range(len(cals))
+            ],
             dtype=np.int32,
         )
         cor_buf[point_index].corr = 1.0
@@ -339,7 +383,8 @@ def write_case_readme(output_case: Path) -> None:
     """Document the generated synthetic case contents."""
     text = """# Synthetic Cavity Case
 
-This case is generated deterministically from the geometry of `test_cavity`, but all observations come from known ground truth.
+This case is generated deterministically from the geometry of `test_cavity`,
+but all observations come from known ground truth.
 
 Contents:
 
@@ -515,4 +560,4 @@ def main(argv: Iterable[str] | None = None) -> int:
 
 
 if __name__ == "__main__":
-    raise SystemExit(main())
\ No newline at end of file
+    raise SystemExit(main())
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index ada532e..02aa34f 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -1784,7 +1784,6 @@ def guarded_two_step_bundle_adjustment(
     reject_on_ray_convergence: bool = True,
 ) -> Tuple[List[Calibration], np.ndarray, Dict[str, object]]:
     """Run pose-only BA then tightly constrained intrinsics BA with acceptance checks."""
-
     if pose_stage_ray_slack < 0:
         raise ValueError("pose_stage_ray_slack must be non-negative")
 
@@ -1847,9 +1846,7 @@ def geometry_stage_ok(
                     "geometry_guard_threshold must be positive when geometry_guard_mode='hard'"
                 )
             return candidate_metric <= geometry_guard_threshold
-        raise ValueError(
-            "geometry_guard_mode must be one of 'off', 'soft', or 'hard'"
-        )
+        raise ValueError("geometry_guard_mode must be one of 'off', 'soft', or 'hard'")
 
     def correspondence_replacement_summary(
         candidate_points: np.ndarray,
@@ -1882,7 +1879,10 @@ def correspondence_replacement_summary(
                 int(correspondence_point_frame_indices[point_index])
             ]
             for camera_index in range(len(candidate_cals)):
-                deltas = frame_targets[camera_index] - projected_pixels[point_index, camera_index]
+                deltas = (
+                    frame_targets[camera_index]
+                    - projected_pixels[point_index, camera_index]
+                )
                 squared_distances = np.sum(deltas * deltas, axis=1)
                 nearest_index = int(np.argmin(squared_distances))
                 replacement_ids[point_index, camera_index] = nearest_index
@@ -1924,7 +1924,10 @@ def correspondence_stage_ok(
                 return True
             return candidate_rate <= prior_rate + 1e-12
         if correspondence_guard_mode == "hard":
-            if correspondence_guard_threshold is None or correspondence_guard_threshold <= 0:
+            if (
+                correspondence_guard_threshold is None
+                or correspondence_guard_threshold <= 0
+            ):
                 raise ValueError(
                     "correspondence_guard_threshold must be positive when correspondence_guard_mode='hard'"
                 )
@@ -2014,12 +2017,17 @@ def correspondence_stage_ok(
         )
         pose_ok = pose_ok and pose_correspondence_ok
     else:
-        release_order = [int(camera_index) for camera_index in pose_release_camera_order]
+        release_order = [
+            int(camera_index) for camera_index in pose_release_camera_order
+        ]
         if not release_order:
             raise ValueError("pose_release_camera_order must not be empty")
         if len(set(release_order)) != len(release_order):
             raise ValueError("pose_release_camera_order must not contain duplicates")
-        if any(camera_index < 0 or camera_index >= len(cals) for camera_index in release_order):
+        if any(
+            camera_index < 0 or camera_index >= len(cals)
+            for camera_index in release_order
+        ):
             raise ValueError(
                 "pose_release_camera_order contains an out-of-range camera index"
             )
@@ -2062,7 +2070,9 @@ def correspondence_stage_ok(
                 known_points=known_points,
                 known_point_sigmas=known_point_sigmas,
             )
-            stage_rms = reprojection_rms(observed_pixels, stage_points, stage_cals, cpar)
+            stage_rms = reprojection_rms(
+                observed_pixels, stage_points, stage_cals, cpar
+            )
             stage_ray_convergence = mean_ray_convergence(
                 observed_pixels, stage_cals, cpar
             )
diff --git a/tests/test_bundle_adjustment.py b/tests/test_bundle_adjustment.py
index a146b70..4a72c78 100644
--- a/tests/test_bundle_adjustment.py
+++ b/tests/test_bundle_adjustment.py
@@ -669,19 +669,21 @@ def test_guarded_two_step_bundle_adjustment_supports_staged_camera_release(self)
         staged_returns = [
             (self.true_cals, points.copy(), {"success": True, "stage": f"pose_{idx}"})
             for idx in range(4)
-        ] + [
-            (self.true_cals, points.copy(), {"success": True, "stage": "intrinsics"})
-        ]
-
-        with patch(
-            "openptv_python.orientation.multi_camera_bundle_adjustment",
-            side_effect=staged_returns,
-        ) as mocked_adjustment, patch(
-            "openptv_python.orientation.reprojection_rms",
-            side_effect=[10.0, 9.0, 8.0, 7.0, 6.0, 5.0],
-        ), patch(
-            "openptv_python.orientation.mean_ray_convergence",
-            side_effect=[6.0, 5.0, 4.0, 3.0, 2.0, 1.0],
+        ] + [(self.true_cals, points.copy(), {"success": True, "stage": "intrinsics"})]
+
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=staged_returns,
+            ) as mocked_adjustment,
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 9.0, 8.0, 7.0, 6.0, 5.0],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[6.0, 5.0, 4.0, 3.0, 2.0, 1.0],
+            ),
         ):
             _, final_points, summary = guarded_two_step_bundle_adjustment(
                 observed_pixels,
@@ -725,12 +727,17 @@ def test_guarded_two_step_bundle_adjustment_supports_staged_camera_release(self)
         self.assertEqual(summary["accepted_stage"], "intrinsics")
         self.assertEqual(len(summary["pose_stage_summaries"]), 4)
         self.assertEqual(
-            [stage["released_camera_index"] for stage in summary["pose_stage_summaries"]],
+            [
+                stage["released_camera_index"]
+                for stage in summary["pose_stage_summaries"]
+            ],
             [0, 1, 2, 3],
         )
         self.assertEqual(final_points.shape, points.shape)
 
-    def test_guarded_two_step_bundle_adjustment_stagewise_ray_slack_allows_near_miss(self):
+    def test_guarded_two_step_bundle_adjustment_stagewise_ray_slack_allows_near_miss(
+        self,
+    ):
         points = np.array(
             [
                 [-10.0, -10.0, 0.0],
@@ -755,19 +762,21 @@ def test_guarded_two_step_bundle_adjustment_stagewise_ray_slack_allows_near_miss
         staged_returns = [
             (self.true_cals, points.copy(), {"success": True, "stage": f"pose_{idx}"})
             for idx in range(4)
-        ] + [
-            (self.true_cals, points.copy(), {"success": True, "stage": "intrinsics"})
-        ]
+        ] + [(self.true_cals, points.copy(), {"success": True, "stage": "intrinsics"})]
 
-        with patch(
-            "openptv_python.orientation.multi_camera_bundle_adjustment",
-            side_effect=staged_returns,
-        ), patch(
-            "openptv_python.orientation.reprojection_rms",
-            side_effect=[10.0, 9.0, 8.0, 7.0, 6.0, 5.5],
-        ), patch(
-            "openptv_python.orientation.mean_ray_convergence",
-            side_effect=[6.0, 5.0, 4.0, 4.0005, 3.5, 3.4],
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=staged_returns,
+            ),
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 9.0, 8.0, 7.0, 6.0, 5.5],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[6.0, 5.0, 4.0, 4.0005, 3.5, 3.4],
+            ),
         ):
             _, _, summary = guarded_two_step_bundle_adjustment(
                 observed_pixels,
@@ -826,27 +835,33 @@ def test_guarded_two_step_bundle_adjustment_rejects_on_hard_geometry_guard(self)
         intrinsics.p1flag = 1
         intrinsics.p2flag = 1
 
-        with patch(
-            "openptv_python.orientation.multi_camera_bundle_adjustment",
-            side_effect=[
-                (pose_cals, points.copy(), {"success": True}),
-                (bad_intrinsic_cals, points.copy(), {"success": True}),
-            ],
-        ), patch(
-            "openptv_python.orientation.reprojection_rms",
-            side_effect=[10.0, 5.0, 4.0],
-        ), patch(
-            "openptv_python.orientation.mean_ray_convergence",
-            side_effect=[3.0, 2.0, 1.0],
-        ), patch(
-            "openptv_python.orientation.img_coord",
-            side_effect=lambda point, cal, _mm: (
-                float(point[0] + cal.get_pos()[0]),
-                float(point[1] + cal.get_pos()[1]),
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=[
+                    (pose_cals, points.copy(), {"success": True}),
+                    (bad_intrinsic_cals, points.copy(), {"success": True}),
+                ],
+            ),
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 5.0, 4.0],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[3.0, 2.0, 1.0],
+            ),
+            patch(
+                "openptv_python.orientation.img_coord",
+                side_effect=lambda point, cal, _mm: (
+                    float(point[0] + cal.get_pos()[0]),
+                    float(point[1] + cal.get_pos()[1]),
+                ),
+            ),
+            patch(
+                "openptv_python.orientation.metric_to_pixel",
+                side_effect=lambda x, y, _cpar: np.array([x, y], dtype=float),
             ),
-        ), patch(
-            "openptv_python.orientation.metric_to_pixel",
-            side_effect=lambda x, y, _cpar: np.array([x, y], dtype=float),
         ):
             final_cals, _, summary = guarded_two_step_bundle_adjustment(
                 observed_pixels,
@@ -891,27 +906,33 @@ def test_guarded_two_step_bundle_adjustment_rejects_on_soft_geometry_guard(self)
         intrinsics.p1flag = 1
         intrinsics.p2flag = 1
 
-        with patch(
-            "openptv_python.orientation.multi_camera_bundle_adjustment",
-            side_effect=[
-                (pose_cals, points.copy(), {"success": True}),
-                (bad_intrinsic_cals, points.copy(), {"success": True}),
-            ],
-        ), patch(
-            "openptv_python.orientation.reprojection_rms",
-            side_effect=[10.0, 5.0, 4.0],
-        ), patch(
-            "openptv_python.orientation.mean_ray_convergence",
-            side_effect=[3.0, 2.0, 1.0],
-        ), patch(
-            "openptv_python.orientation.img_coord",
-            side_effect=lambda point, cal, _mm: (
-                float(point[0] + cal.get_pos()[0]),
-                float(point[1] + cal.get_pos()[1]),
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=[
+                    (pose_cals, points.copy(), {"success": True}),
+                    (bad_intrinsic_cals, points.copy(), {"success": True}),
+                ],
+            ),
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 5.0, 4.0],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[3.0, 2.0, 1.0],
+            ),
+            patch(
+                "openptv_python.orientation.img_coord",
+                side_effect=lambda point, cal, _mm: (
+                    float(point[0] + cal.get_pos()[0]),
+                    float(point[1] + cal.get_pos()[1]),
+                ),
+            ),
+            patch(
+                "openptv_python.orientation.metric_to_pixel",
+                side_effect=lambda x, y, _cpar: np.array([x, y], dtype=float),
             ),
-        ), patch(
-            "openptv_python.orientation.metric_to_pixel",
-            side_effect=lambda x, y, _cpar: np.array([x, y], dtype=float),
         ):
             final_cals, _, summary = guarded_two_step_bundle_adjustment(
                 observed_pixels,
@@ -935,7 +956,9 @@ def test_guarded_two_step_bundle_adjustment_rejects_on_soft_geometry_guard(self)
         )
         np.testing.assert_allclose(final_cals[3].get_pos(), pose_cals[3].get_pos())
 
-    def test_guarded_two_step_bundle_adjustment_rejects_on_hard_correspondence_guard(self):
+    def test_guarded_two_step_bundle_adjustment_rejects_on_hard_correspondence_guard(
+        self,
+    ):
         points = np.array(
             [
                 [0.0, 0.0, 0.0],
@@ -968,24 +991,30 @@ def test_guarded_two_step_bundle_adjustment_rejects_on_hard_correspondence_guard
             for _ in range(4)
         ]
 
-        with patch(
-            "openptv_python.orientation.multi_camera_bundle_adjustment",
-            side_effect=[
-                (pose_cals, points.copy(), {"success": True}),
-                (bad_intrinsic_cals, points.copy(), {"success": True}),
-            ],
-        ), patch(
-            "openptv_python.orientation.reprojection_rms",
-            side_effect=[10.0, 5.0, 4.0],
-        ), patch(
-            "openptv_python.orientation.mean_ray_convergence",
-            side_effect=[3.0, 2.0, 1.0],
-        ), patch(
-            "openptv_python.orientation.image_coordinates",
-            side_effect=lambda pts, cal, _mm: pts[:, :2] + cal.get_pos()[:2],
-        ), patch(
-            "openptv_python.orientation.arr_metric_to_pixel",
-            side_effect=lambda coords, _cpar: coords,
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=[
+                    (pose_cals, points.copy(), {"success": True}),
+                    (bad_intrinsic_cals, points.copy(), {"success": True}),
+                ],
+            ),
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 5.0, 4.0],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[3.0, 2.0, 1.0],
+            ),
+            patch(
+                "openptv_python.orientation.image_coordinates",
+                side_effect=lambda pts, cal, _mm: pts[:, :2] + cal.get_pos()[:2],
+            ),
+            patch(
+                "openptv_python.orientation.arr_metric_to_pixel",
+                side_effect=lambda coords, _cpar: coords,
+            ),
         ):
             final_cals, _, summary = guarded_two_step_bundle_adjustment(
                 observed_pixels,
@@ -995,7 +1024,9 @@ def test_guarded_two_step_bundle_adjustment_rejects_on_hard_correspondence_guard
                 intrinsics,
                 point_init=points,
                 fixed_camera_indices=[0, 1],
-                correspondence_original_ids=np.tile(np.arange(len(points))[:, None], (1, 4)),
+                correspondence_original_ids=np.tile(
+                    np.arange(len(points))[:, None], (1, 4)
+                ),
                 correspondence_point_frame_indices=np.zeros(len(points), dtype=int),
                 correspondence_frame_target_pixels=[target_sets],
                 correspondence_guard_mode="hard",
@@ -1008,7 +1039,9 @@ def test_guarded_two_step_bundle_adjustment_rejects_on_hard_correspondence_guard
         self.assertGreater(summary["intrinsic_correspondence_rate"], 0.2)
         np.testing.assert_allclose(final_cals[2].get_pos(), pose_cals[2].get_pos())
 
-    def test_guarded_two_step_bundle_adjustment_rejects_on_soft_correspondence_guard(self):
+    def test_guarded_two_step_bundle_adjustment_rejects_on_soft_correspondence_guard(
+        self,
+    ):
         points = np.array(
             [
                 [0.0, 0.0, 0.0],
@@ -1041,24 +1074,30 @@ def test_guarded_two_step_bundle_adjustment_rejects_on_soft_correspondence_guard
             for _ in range(4)
         ]
 
-        with patch(
-            "openptv_python.orientation.multi_camera_bundle_adjustment",
-            side_effect=[
-                (pose_cals, points.copy(), {"success": True}),
-                (bad_intrinsic_cals, points.copy(), {"success": True}),
-            ],
-        ), patch(
-            "openptv_python.orientation.reprojection_rms",
-            side_effect=[10.0, 5.0, 4.0],
-        ), patch(
-            "openptv_python.orientation.mean_ray_convergence",
-            side_effect=[3.0, 2.0, 1.0],
-        ), patch(
-            "openptv_python.orientation.image_coordinates",
-            side_effect=lambda pts, cal, _mm: pts[:, :2] + cal.get_pos()[:2],
-        ), patch(
-            "openptv_python.orientation.arr_metric_to_pixel",
-            side_effect=lambda coords, _cpar: coords,
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=[
+                    (pose_cals, points.copy(), {"success": True}),
+                    (bad_intrinsic_cals, points.copy(), {"success": True}),
+                ],
+            ),
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 5.0, 4.0],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[3.0, 2.0, 1.0],
+            ),
+            patch(
+                "openptv_python.orientation.image_coordinates",
+                side_effect=lambda pts, cal, _mm: pts[:, :2] + cal.get_pos()[:2],
+            ),
+            patch(
+                "openptv_python.orientation.arr_metric_to_pixel",
+                side_effect=lambda coords, _cpar: coords,
+            ),
         ):
             final_cals, _, summary = guarded_two_step_bundle_adjustment(
                 observed_pixels,
@@ -1068,7 +1107,9 @@ def test_guarded_two_step_bundle_adjustment_rejects_on_soft_correspondence_guard
                 intrinsics,
                 point_init=points,
                 fixed_camera_indices=[0, 1],
-                correspondence_original_ids=np.tile(np.arange(len(points))[:, None], (1, 4)),
+                correspondence_original_ids=np.tile(
+                    np.arange(len(points))[:, None], (1, 4)
+                ),
                 correspondence_point_frame_indices=np.zeros(len(points), dtype=int),
                 correspondence_frame_target_pixels=[target_sets],
                 correspondence_guard_mode="soft",
@@ -1160,7 +1201,9 @@ def test_cavity_intrinsics_only_improves_from_intrinsic_perturbation(self):
         self.assertLess(after_rms, before_rms - 0.1)
         for refined, start in zip(refined_cals, start_cals):
             np.testing.assert_allclose(refined.get_pos(), start.get_pos(), atol=1e-12)
-            np.testing.assert_allclose(refined.get_angles(), start.get_angles(), atol=1e-12)
+            np.testing.assert_allclose(
+                refined.get_angles(), start.get_angles(), atol=1e-12
+            )
         np.testing.assert_allclose(refined_points, point_init)
 
 
diff --git a/tests/test_demo_bundle_adjustment.py b/tests/test_demo_bundle_adjustment.py
index cd21f0e..c68978f 100644
--- a/tests/test_demo_bundle_adjustment.py
+++ b/tests/test_demo_bundle_adjustment.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 
+from openptv_python.calibration import read_calibration
 from openptv_python.demo_bundle_adjustment import (
     ExperimentResult,
     all_fixed_camera_pairs,
@@ -19,7 +20,6 @@
     summarize_fixed_camera_diagnostics,
     summarize_quadruplet_sensitivity,
 )
-from openptv_python.calibration import read_calibration
 from openptv_python.parameters import ControlPar, read_volume_par
 from openptv_python.tracking_frame_buf import read_path_frame, read_targets
 
@@ -69,14 +69,10 @@ def test_default_experiments_adds_known_point_presets(self):
             spec for spec in experiments if spec.name == "pose_trf_known_points"
         )
         guarded_spec = next(
-            spec
-            for spec in experiments
-            if spec.name == "guarded_two_step_known_points"
+            spec for spec in experiments if spec.name == "guarded_two_step_known_points"
         )
         staged_spec = next(
-            spec
-            for spec in experiments
-            if spec.name == "guarded_stagewise_release"
+            spec for spec in experiments if spec.name == "guarded_stagewise_release"
         )
         staged_known_spec = next(
             spec
@@ -88,7 +84,9 @@ def test_default_experiments_adds_known_point_presets(self):
         self.assertIs(guarded_spec.ba_kwargs["known_points"], known_points)
         self.assertEqual(guarded_spec.ba_kwargs["known_point_sigmas"], 0.25)
         self.assertEqual(staged_spec.ba_kwargs["fixed_camera_indices"], [1, 2, 3])
-        self.assertEqual(staged_spec.ba_kwargs["pose_release_camera_order"], [0, 1, 2, 3])
+        self.assertEqual(
+            staged_spec.ba_kwargs["pose_release_camera_order"], [0, 1, 2, 3]
+        )
         self.assertEqual(staged_spec.ba_kwargs["pose_stage_ray_slack"], 0.0)
         self.assertIs(staged_known_spec.ba_kwargs["known_points"], known_points)
         self.assertEqual(staged_known_spec.ba_kwargs["known_point_sigmas"], 0.25)
@@ -126,7 +124,9 @@ def test_default_experiments_accepts_geometry_guard_configuration(self):
             guarded_spec.ba_kwargs["correspondence_guard_reference_rate"],
             0.15625,
         )
-        self.assertEqual(staged_spec.ba_kwargs["pose_release_camera_order"], [2, 0, 1, 3])
+        self.assertEqual(
+            staged_spec.ba_kwargs["pose_release_camera_order"], [2, 0, 1, 3]
+        )
         self.assertEqual(staged_spec.ba_kwargs["fixed_camera_indices"], [0, 1, 3])
         self.assertEqual(staged_spec.ba_kwargs["pose_stage_ray_slack"], 0.002)
 
@@ -242,7 +242,9 @@ def test_epipolar_and_quadruplet_diagnostics_detect_perturbation(self):
             for cam_num in range(1, 5)
         ]
 
-        cor_buf, path_buf = read_path_frame(str(cavity_dir / "res_orig/rt_is"), "", "", 10001)
+        cor_buf, path_buf = read_path_frame(
+            str(cavity_dir / "res_orig/rt_is"), "", "", 10001
+        )
         targets = [
             read_targets(str(cavity_dir / f"img_orig/cam{cam_num}.%05d"), 10001)
             for cam_num in range(1, 5)
@@ -283,8 +285,10 @@ def test_epipolar_and_quadruplet_diagnostics_detect_perturbation(self):
             max(item.max_distance for item in baseline_epipolar),
         )
         self.assertGreater(perturbed_quad.max_spread, baseline_quad.max_spread)
-        self.assertIn("baseline", format_quadruplet_sensitivity(baseline_quad, perturbed_quad))
+        self.assertIn(
+            "baseline", format_quadruplet_sensitivity(baseline_quad, perturbed_quad)
+        )
 
 
 if __name__ == "__main__":
-    unittest.main()
\ No newline at end of file
+    unittest.main()
diff --git a/tests/test_synthetic_cavity_case.py b/tests/test_synthetic_cavity_case.py
index f2ca0bb..50f0d87 100644
--- a/tests/test_synthetic_cavity_case.py
+++ b/tests/test_synthetic_cavity_case.py
@@ -146,11 +146,16 @@ def test_synthetic_case_bundle_adjustment_recovers_ground_truth_from_controlled_
     point_init = truth_points + np.array([0.35, -0.25, 0.18])
 
     initial_camera_errors = [
-        np.linalg.norm(start_cals[camera_index].get_pos() - truth_cals[camera_index].get_pos())
+        np.linalg.norm(
+            start_cals[camera_index].get_pos() - truth_cals[camera_index].get_pos()
+        )
         for camera_index in (2, 3)
     ]
     initial_angle_errors = [
-        np.linalg.norm(start_cals[camera_index].get_angles() - truth_cals[camera_index].get_angles())
+        np.linalg.norm(
+            start_cals[camera_index].get_angles()
+            - truth_cals[camera_index].get_angles()
+        )
         for camera_index in (2, 3)
     ]
     initial_point_error = float(
@@ -178,11 +183,16 @@ def test_synthetic_case_bundle_adjustment_recovers_ground_truth_from_controlled_
     )
 
     final_camera_errors = [
-        np.linalg.norm(refined_cals[camera_index].get_pos() - truth_cals[camera_index].get_pos())
+        np.linalg.norm(
+            refined_cals[camera_index].get_pos() - truth_cals[camera_index].get_pos()
+        )
         for camera_index in (2, 3)
     ]
     final_angle_errors = [
-        np.linalg.norm(refined_cals[camera_index].get_angles() - truth_cals[camera_index].get_angles())
+        np.linalg.norm(
+            refined_cals[camera_index].get_angles()
+            - truth_cals[camera_index].get_angles()
+        )
         for camera_index in (2, 3)
     ]
     final_point_error = float(
@@ -267,4 +277,4 @@ def test_synthetic_case_intrinsics_only_recovers_ground_truth_from_controlled_pe
     assert max(k1_errors) < 1.5e-5
     assert max(p1_errors) < 1.5e-5
     assert max(p2_errors) < 3.0e-5
-    assert result["final_reprojection_rms"] < result["initial_reprojection_rms"] * 0.2
\ No newline at end of file
+    assert result["final_reprojection_rms"] < result["initial_reprojection_rms"] * 0.2

From 95f9fa3e1ff21581ba0e4e1ad494368b49755ef9 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 13:38:13 +0200
Subject: [PATCH 11/27] smaller steps bundle adjustment

---
 openptv_python/demo_bundle_adjustment.py | 432 +++++++++++++++++++++
 openptv_python/orientation.py            | 463 ++++++++++++++++-------
 tests/test_bundle_adjustment.py          | 175 +++++++++
 tests/test_demo_bundle_adjustment.py     | 112 ++++++
 4 files changed, 1054 insertions(+), 128 deletions(-)

diff --git a/openptv_python/demo_bundle_adjustment.py b/openptv_python/demo_bundle_adjustment.py
index 4b44b0a..5eac602 100644
--- a/openptv_python/demo_bundle_adjustment.py
+++ b/openptv_python/demo_bundle_adjustment.py
@@ -995,6 +995,145 @@ def default_experiments(
         "phi": (-0.02, 0.02),
         "kappa": (-0.02, 0.02),
     }
+    conservative_pose_stage_configs = [
+        {
+            "prior_sigmas": {
+                "x0": 0.05,
+                "y0": 0.05,
+                "z0": 0.05,
+                "omega": 5e-4,
+                "phi": 5e-4,
+                "kappa": 5e-4,
+            },
+            "parameter_bounds": {
+                "x0": (-0.1, 0.1),
+                "y0": (-0.1, 0.1),
+                "z0": (-0.1, 0.1),
+                "omega": (-0.002, 0.002),
+                "phi": (-0.002, 0.002),
+                "kappa": (-0.002, 0.002),
+            },
+            "max_nfev": 4,
+            "optimize_points": False,
+            "x_scale": {
+                "x0": 0.02,
+                "y0": 0.02,
+                "z0": 0.02,
+                "omega": 2e-4,
+                "phi": 2e-4,
+                "kappa": 2e-4,
+            },
+        },
+        {
+            "prior_sigmas": {
+                "x0": 0.1,
+                "y0": 0.1,
+                "z0": 0.1,
+                "omega": 1e-3,
+                "phi": 1e-3,
+                "kappa": 1e-3,
+            },
+            "parameter_bounds": {
+                "x0": (-0.2, 0.2),
+                "y0": (-0.2, 0.2),
+                "z0": (-0.2, 0.2),
+                "omega": (-0.004, 0.004),
+                "phi": (-0.004, 0.004),
+                "kappa": (-0.004, 0.004),
+            },
+            "max_nfev": 4,
+            "optimize_points": True,
+            "x_scale": {
+                "x0": 0.03,
+                "y0": 0.03,
+                "z0": 0.03,
+                "omega": 3e-4,
+                "phi": 3e-4,
+                "kappa": 3e-4,
+            },
+        },
+        {
+            "prior_sigmas": pose_priors,
+            "parameter_bounds": pose_bounds,
+            "max_nfev": 8,
+            "optimize_points": True,
+            "x_scale": {
+                "x0": 0.05,
+                "y0": 0.05,
+                "z0": 0.05,
+                "omega": 5e-4,
+                "phi": 5e-4,
+                "kappa": 5e-4,
+                "points": 0.1,
+            },
+        },
+    ]
+    intrinsics_first_pose_stage_configs = [
+        {
+            "prior_sigmas": {
+                "x0": 0.02,
+                "y0": 0.02,
+                "z0": 0.02,
+                "omega": 2e-4,
+                "phi": 2e-4,
+                "kappa": 2e-4,
+            },
+            "parameter_bounds": {
+                "x0": (-0.05, 0.05),
+                "y0": (-0.05, 0.05),
+                "z0": (-0.05, 0.05),
+                "omega": (-0.001, 0.001),
+                "phi": (-0.001, 0.001),
+                "kappa": (-0.001, 0.001),
+            },
+            "max_nfev": 4,
+            "optimize_points": False,
+            "x_scale": {
+                "x0": 0.01,
+                "y0": 0.01,
+                "z0": 0.01,
+                "omega": 1e-4,
+                "phi": 1e-4,
+                "kappa": 1e-4,
+            },
+        },
+        {
+            "prior_sigmas": {
+                "x0": 0.05,
+                "y0": 0.05,
+                "z0": 0.05,
+                "omega": 5e-4,
+                "phi": 5e-4,
+                "kappa": 5e-4,
+            },
+            "parameter_bounds": {
+                "x0": (-0.1, 0.1),
+                "y0": (-0.1, 0.1),
+                "z0": (-0.1, 0.1),
+                "omega": (-0.002, 0.002),
+                "phi": (-0.002, 0.002),
+                "kappa": (-0.002, 0.002),
+            },
+            "max_nfev": 4,
+            "optimize_points": True,
+            "x_scale": {
+                "x0": 0.02,
+                "y0": 0.02,
+                "z0": 0.02,
+                "omega": 2e-4,
+                "phi": 2e-4,
+                "kappa": 2e-4,
+                "points": 0.1,
+            },
+        },
+    ]
+    known_point_pose_stage_configs = [
+        {
+            **stage_config,
+            "optimize_points": True,
+        }
+        for stage_config in conservative_pose_stage_configs
+    ]
     tight_intrinsic_priors = {
         "k1": 1e-12,
         "k2": 1e-12,
@@ -1116,6 +1255,16 @@ def default_experiments(
                 "pose_prior_sigmas": pose_priors,
                 "pose_parameter_bounds": pose_bounds,
                 "pose_max_nfev": 8,
+                "pose_x_scale": {
+                    "x0": 0.05,
+                    "y0": 0.05,
+                    "z0": 0.05,
+                    "omega": 5e-4,
+                    "phi": 5e-4,
+                    "kappa": 5e-4,
+                    "points": 0.1,
+                },
+                "pose_stage_configs": conservative_pose_stage_configs,
                 "intrinsic_prior_sigmas": tight_intrinsic_priors,
                 "intrinsic_parameter_bounds": tight_intrinsic_bounds,
                 "intrinsic_max_nfev": 4,
@@ -1139,6 +1288,69 @@ def default_experiments(
                 "pose_prior_sigmas": pose_priors,
                 "pose_parameter_bounds": pose_bounds,
                 "pose_max_nfev": 8,
+                "pose_x_scale": {
+                    "x0": 0.05,
+                    "y0": 0.05,
+                    "z0": 0.05,
+                    "omega": 5e-4,
+                    "phi": 5e-4,
+                    "kappa": 5e-4,
+                    "points": 0.1,
+                },
+                "pose_stage_configs": conservative_pose_stage_configs,
+                "intrinsic_prior_sigmas": tight_intrinsic_priors,
+                "intrinsic_parameter_bounds": tight_intrinsic_bounds,
+                "intrinsic_max_nfev": 4,
+                "geometry_guard_mode": geometry_guard_mode,
+                "geometry_guard_threshold": geometry_guard_threshold,
+                "correspondence_guard_mode": correspondence_guard_mode,
+                "correspondence_guard_threshold": correspondence_guard_threshold,
+                "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
+            },
+        ),
+        ExperimentSpec(
+            name="intrinsics_first_guarded_stagewise_release",
+            description=(
+                "Intrinsics-only warm start followed by a tiny guarded "
+                "stagewise pose release"
+            ),
+            mode="intrinsics_then_guarded",
+            ba_kwargs={
+                "warmstart_orient_par": intrinsics_only,
+                "warmstart_fixed_camera_indices": list(range(num_cams)),
+                "warmstart_loss": "linear",
+                "warmstart_method": "trf",
+                "warmstart_prior_sigmas": {
+                    "k1": 5e-5,
+                    "p1": 1e-4,
+                    "p2": 1e-4,
+                },
+                "warmstart_parameter_bounds": {
+                    "k1": (-5e-5, 5e-5),
+                    "p1": (-2e-4, 2e-4),
+                    "p2": (-2e-4, 2e-4),
+                },
+                "warmstart_max_nfev": 40,
+                "warmstart_optimize_extrinsics": False,
+                "warmstart_optimize_points": False,
+                "pose_orient_par": OrientPar(),
+                "intrinsic_orient_par": intrinsics,
+                "fixed_camera_indices": staged_fixed,
+                "pose_release_camera_order": staged_order,
+                "pose_stage_ray_slack": pose_stage_ray_slack,
+                "pose_prior_sigmas": pose_priors,
+                "pose_parameter_bounds": pose_bounds,
+                "pose_max_nfev": 4,
+                "pose_x_scale": {
+                    "x0": 0.02,
+                    "y0": 0.02,
+                    "z0": 0.02,
+                    "omega": 2e-4,
+                    "phi": 2e-4,
+                    "kappa": 2e-4,
+                    "points": 0.1,
+                },
+                "pose_stage_configs": intrinsics_first_pose_stage_configs,
                 "intrinsic_prior_sigmas": tight_intrinsic_priors,
                 "intrinsic_parameter_bounds": tight_intrinsic_bounds,
                 "intrinsic_max_nfev": 4,
@@ -1181,6 +1393,16 @@ def default_experiments(
                         "pose_prior_sigmas": pose_priors,
                         "pose_parameter_bounds": pose_bounds,
                         "pose_max_nfev": 8,
+                        "pose_x_scale": {
+                            "x0": 0.05,
+                            "y0": 0.05,
+                            "z0": 0.05,
+                            "omega": 5e-4,
+                            "phi": 5e-4,
+                            "kappa": 5e-4,
+                            "points": 0.1,
+                        },
+                        "pose_stage_configs": known_point_pose_stage_configs,
                         "intrinsic_prior_sigmas": tight_intrinsic_priors,
                         "intrinsic_parameter_bounds": tight_intrinsic_bounds,
                         "intrinsic_max_nfev": 4,
@@ -1206,6 +1428,16 @@ def default_experiments(
                         "pose_prior_sigmas": pose_priors,
                         "pose_parameter_bounds": pose_bounds,
                         "pose_max_nfev": 8,
+                        "pose_x_scale": {
+                            "x0": 0.05,
+                            "y0": 0.05,
+                            "z0": 0.05,
+                            "omega": 5e-4,
+                            "phi": 5e-4,
+                            "kappa": 5e-4,
+                            "points": 0.1,
+                        },
+                        "pose_stage_configs": known_point_pose_stage_configs,
                         "intrinsic_prior_sigmas": tight_intrinsic_priors,
                         "intrinsic_parameter_bounds": tight_intrinsic_bounds,
                         "intrinsic_max_nfev": 4,
@@ -1307,6 +1539,10 @@ def run_experiment(
                 float | np.ndarray | None,
                 spec.ba_kwargs.get("known_point_sigmas"),
             ),
+            x_scale=cast(
+                float | Sequence[float] | Dict[str, float] | None,
+                spec.ba_kwargs.get("x_scale"),
+            ),
             ftol=cast(float | None, spec.ba_kwargs.get("ftol")),
             xtol=cast(float | None, spec.ba_kwargs.get("xtol")),
             gtol=cast(float | None, spec.ba_kwargs.get("gtol")),
@@ -1348,6 +1584,14 @@ def run_experiment(
             pose_loss=cast(str, spec.ba_kwargs.get("pose_loss", "linear")),
             pose_method=cast(str, spec.ba_kwargs.get("pose_method", "trf")),
             pose_max_nfev=cast(int | None, spec.ba_kwargs.get("pose_max_nfev")),
+            pose_x_scale=cast(
+                float | Sequence[float] | Dict[str, float] | None,
+                spec.ba_kwargs.get("pose_x_scale"),
+            ),
+            pose_stage_configs=cast(
+                Sequence[Dict[str, object]] | None,
+                spec.ba_kwargs.get("pose_stage_configs"),
+            ),
             intrinsic_prior_sigmas=cast(
                 Dict[str, float] | None,
                 spec.ba_kwargs.get("intrinsic_prior_sigmas"),
@@ -1368,6 +1612,10 @@ def run_experiment(
                 int | None,
                 spec.ba_kwargs.get("intrinsic_max_nfev"),
             ),
+            intrinsic_x_scale=cast(
+                float | Sequence[float] | Dict[str, float] | None,
+                spec.ba_kwargs.get("intrinsic_x_scale"),
+            ),
             intrinsic_ftol=cast(
                 float | None, spec.ba_kwargs.get("intrinsic_ftol", 1e-12)
             ),
@@ -1437,6 +1685,190 @@ def run_experiment(
         final_rms = cast(float, summary["final_reprojection_rms"])
         final_ray = cast(float, summary["final_mean_ray_convergence"])
         notes = f"accepted_stage={summary['accepted_stage']}"
+    elif spec.mode == "intrinsics_then_guarded":
+        warmstart_orient_par = cast(OrientPar, spec.ba_kwargs["warmstart_orient_par"])
+        warmstart_cals, warmstart_points, warmstart_result = multi_camera_bundle_adjustment(
+            observed_pixels,
+            working_cals,
+            control,
+            warmstart_orient_par,
+            point_init=point_init,
+            fix_first_camera=cast(bool, spec.ba_kwargs.get("fix_first_camera", True)),
+            fixed_camera_indices=cast(
+                List[int] | None,
+                spec.ba_kwargs.get("warmstart_fixed_camera_indices"),
+            ),
+            loss=cast(str, spec.ba_kwargs.get("warmstart_loss", "linear")),
+            method=cast(str, spec.ba_kwargs.get("warmstart_method", "trf")),
+            prior_sigmas=cast(
+                Dict[str, float] | None,
+                spec.ba_kwargs.get("warmstart_prior_sigmas"),
+            ),
+            parameter_bounds=cast(
+                Dict[str, tuple[float, float]] | None,
+                spec.ba_kwargs.get("warmstart_parameter_bounds"),
+            ),
+            max_nfev=cast(int | None, spec.ba_kwargs.get("warmstart_max_nfev")),
+            optimize_extrinsics=cast(
+                bool,
+                spec.ba_kwargs.get("warmstart_optimize_extrinsics", False),
+            ),
+            optimize_points=cast(
+                bool,
+                spec.ba_kwargs.get("warmstart_optimize_points", False),
+            ),
+            x_scale=cast(
+                float | Sequence[float] | Dict[str, float] | None,
+                spec.ba_kwargs.get("warmstart_x_scale"),
+            ),
+        )
+        pose_orient_par = cast(OrientPar, spec.ba_kwargs["pose_orient_par"])
+        intrinsic_orient_par = cast(OrientPar, spec.ba_kwargs["intrinsic_orient_par"])
+        refined_cals, refined_points, summary = guarded_two_step_bundle_adjustment(
+            observed_pixels,
+            warmstart_cals,
+            control,
+            pose_orient_par,
+            intrinsic_orient_par,
+            point_init=warmstart_points,
+            fixed_camera_indices=cast(
+                List[int] | None,
+                spec.ba_kwargs.get("fixed_camera_indices"),
+            ),
+            pose_release_camera_order=cast(
+                List[int] | None,
+                spec.ba_kwargs.get("pose_release_camera_order"),
+            ),
+            pose_stage_ray_slack=cast(
+                float,
+                spec.ba_kwargs.get("pose_stage_ray_slack", 0.0),
+            ),
+            pose_prior_sigmas=cast(
+                Dict[str, float] | None,
+                spec.ba_kwargs.get("pose_prior_sigmas"),
+            ),
+            pose_parameter_bounds=cast(
+                Dict[str, tuple[float, float]] | None,
+                spec.ba_kwargs.get("pose_parameter_bounds"),
+            ),
+            pose_loss=cast(str, spec.ba_kwargs.get("pose_loss", "linear")),
+            pose_method=cast(str, spec.ba_kwargs.get("pose_method", "trf")),
+            pose_max_nfev=cast(int | None, spec.ba_kwargs.get("pose_max_nfev")),
+            pose_x_scale=cast(
+                float | Sequence[float] | Dict[str, float] | None,
+                spec.ba_kwargs.get("pose_x_scale"),
+            ),
+            pose_stage_configs=cast(
+                Sequence[Dict[str, object]] | None,
+                spec.ba_kwargs.get("pose_stage_configs"),
+            ),
+            intrinsic_prior_sigmas=cast(
+                Dict[str, float] | None,
+                spec.ba_kwargs.get("intrinsic_prior_sigmas"),
+            ),
+            intrinsic_parameter_bounds=cast(
+                Dict[str, tuple[float, float]] | None,
+                spec.ba_kwargs.get("intrinsic_parameter_bounds"),
+            ),
+            intrinsic_loss=cast(
+                str,
+                spec.ba_kwargs.get("intrinsic_loss", "linear"),
+            ),
+            intrinsic_method=cast(
+                str,
+                spec.ba_kwargs.get("intrinsic_method", "trf"),
+            ),
+            intrinsic_max_nfev=cast(
+                int | None,
+                spec.ba_kwargs.get("intrinsic_max_nfev"),
+            ),
+            intrinsic_x_scale=cast(
+                float | Sequence[float] | Dict[str, float] | None,
+                spec.ba_kwargs.get("intrinsic_x_scale"),
+            ),
+            intrinsic_ftol=cast(
+                float | None, spec.ba_kwargs.get("intrinsic_ftol", 1e-12)
+            ),
+            intrinsic_xtol=cast(
+                float | None, spec.ba_kwargs.get("intrinsic_xtol", 1e-12)
+            ),
+            intrinsic_gtol=cast(
+                float | None, spec.ba_kwargs.get("intrinsic_gtol", 1e-12)
+            ),
+            pose_optimize_points=cast(
+                bool,
+                spec.ba_kwargs.get("pose_optimize_points", True),
+            ),
+            intrinsic_optimize_points=cast(
+                bool,
+                spec.ba_kwargs.get("intrinsic_optimize_points", True),
+            ),
+            reject_worse_solution=cast(
+                bool,
+                spec.ba_kwargs.get("reject_worse_solution", True),
+            ),
+            reject_on_ray_convergence=cast(
+                bool,
+                spec.ba_kwargs.get("reject_on_ray_convergence", True),
+            ),
+            known_points=cast(
+                Dict[int, np.ndarray] | None,
+                spec.ba_kwargs.get("known_points"),
+            ),
+            known_point_sigmas=cast(
+                float | np.ndarray | None,
+                spec.ba_kwargs.get("known_point_sigmas"),
+            ),
+            geometry_reference_points=reference_geometry_points,
+            geometry_reference_cals=reference_cals,
+            geometry_guard_mode=cast(
+                str,
+                spec.ba_kwargs.get("geometry_guard_mode", "off"),
+            ),
+            geometry_guard_threshold=cast(
+                float | None,
+                spec.ba_kwargs.get("geometry_guard_threshold"),
+            ),
+            correspondence_original_ids=(
+                None if tracking_data is None else tracking_data.original_target_ids
+            ),
+            correspondence_point_frame_indices=(
+                None if tracking_data is None else tracking_data.point_frame_indices
+            ),
+            correspondence_frame_target_pixels=(
+                None if tracking_data is None else tracking_data.frame_target_pixels
+            ),
+            correspondence_guard_mode=cast(
+                str,
+                spec.ba_kwargs.get("correspondence_guard_mode", "off"),
+            ),
+            correspondence_guard_threshold=cast(
+                float | None,
+                spec.ba_kwargs.get("correspondence_guard_threshold"),
+            ),
+            correspondence_guard_reference_rate=cast(
+                float | None,
+                spec.ba_kwargs.get("correspondence_guard_reference_rate"),
+            ),
+        )
+        warmstart_success = bool(
+            getattr(
+                warmstart_result,
+                "success",
+                cast(Dict[str, object], warmstart_result).get("success", False),
+            )
+        )
+        final_rms = cast(float, summary["final_reprojection_rms"])
+        final_ray = cast(float, summary["final_mean_ray_convergence"])
+        warmstart_rms = float(
+            cast(Dict[str, object], warmstart_result)["final_reprojection_rms"]
+        )
+        success = warmstart_success
+        notes = (
+            f"warmstart_success={warmstart_success}; "
+            f"warmstart_rms={warmstart_rms:.6f}; "
+            f"accepted_stage={summary['accepted_stage']}"
+        )
     else:
         raise ValueError(f"Unknown experiment mode: {spec.mode}")
 
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index 02aa34f..1074deb 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -1361,6 +1361,53 @@ def _expand_parameter_limits(
     return np.asarray(lower, dtype=np.float64), np.asarray(upper, dtype=np.float64)
 
 
+def _bundle_adjustment_x_scale(
+    x_scale: Optional[float | Sequence[float] | Dict[str, float]],
+    parameter_names: List[str],
+    optimized_cam_indices: List[int],
+    optimize_points: bool,
+    num_points: int,
+    total_parameters: int,
+) -> Optional[float | np.ndarray]:
+    """Normalize least_squares x_scale inputs to the packed BA parameter vector."""
+    if x_scale is None:
+        return None
+
+    if np.isscalar(x_scale):
+        value = float(cast(float, x_scale))
+        if value <= 0:
+            raise ValueError("x_scale must be strictly positive")
+        return value
+
+    if isinstance(x_scale, dict):
+        values: List[float] = []
+        for _cam in optimized_cam_indices:
+            for name in parameter_names:
+                value = float(x_scale.get(name, 1.0))
+                if value <= 0:
+                    raise ValueError("x_scale entries must be strictly positive")
+                values.append(value)
+
+        if optimize_points:
+            point_scale = float(x_scale.get("points", x_scale.get("point", 1.0)))
+            if point_scale <= 0:
+                raise ValueError("point x_scale must be strictly positive")
+            values.extend([point_scale] * (num_points * 3))
+
+        return np.asarray(values, dtype=np.float64)
+
+    values = np.asarray(x_scale, dtype=np.float64)
+    if values.ndim != 1:
+        raise ValueError("x_scale sequence must be one-dimensional")
+    if values.size != total_parameters:
+        raise ValueError(
+            f"x_scale sequence must have length {total_parameters}, got {values.size}"
+        )
+    if np.any(values <= 0):
+        raise ValueError("x_scale entries must be strictly positive")
+    return values.copy()
+
+
 def _bundle_adjustment_jacobian_sparsity(
     obs_mask: np.ndarray,
     num_cams: int,
@@ -1478,6 +1525,7 @@ def multi_camera_bundle_adjustment(
     optimize_points: bool = True,
     known_points: Optional[Dict[int, np.ndarray]] = None,
     known_point_sigmas: Optional[float | np.ndarray] = None,
+    x_scale: Optional[float | Sequence[float] | Dict[str, float]] = None,
     ftol: Optional[float] = None,
     xtol: Optional[float] = None,
     gtol: Optional[float] = None,
@@ -1630,6 +1678,15 @@ def multi_camera_bundle_adjustment(
     else:
         bounds = (camera_lower, camera_upper)
 
+    normalized_x_scale = _bundle_adjustment_x_scale(
+        x_scale,
+        parameter_names,
+        optimized_cam_indices,
+        optimize_points,
+        num_points,
+        x0.size,
+    )
+
     def unpack_parameters(params: np.ndarray) -> Tuple[List[Calibration], np.ndarray]:
         trial_cals = [_clone_calibration(cal) for cal in base_cals]
         offset = 0
@@ -1701,6 +1758,8 @@ def residual_vector(params: np.ndarray) -> np.ndarray:
         "max_nfev": max_nfev,
         "bounds": bounds,
     }
+    if normalized_x_scale is not None:
+        least_squares_kwargs["x_scale"] = normalized_x_scale
     if method != "lm" and x0.size > 0:
         least_squares_kwargs["jac_sparsity"] = _bundle_adjustment_jacobian_sparsity(
             obs_mask,
@@ -1758,11 +1817,14 @@ def guarded_two_step_bundle_adjustment(
     pose_loss: str = "linear",
     pose_method: str = "trf",
     pose_max_nfev: Optional[int] = None,
+    pose_x_scale: Optional[float | Sequence[float] | Dict[str, float]] = None,
+    pose_stage_configs: Optional[Sequence[Dict[str, object]]] = None,
     intrinsic_prior_sigmas: Optional[Dict[str, float]] = None,
     intrinsic_parameter_bounds: Optional[Dict[str, Tuple[float, float]]] = None,
     intrinsic_loss: str = "linear",
     intrinsic_method: str = "trf",
     intrinsic_max_nfev: Optional[int] = None,
+    intrinsic_x_scale: Optional[float | Sequence[float] | Dict[str, float]] = None,
     intrinsic_ftol: Optional[float] = 1e-12,
     intrinsic_xtol: Optional[float] = 1e-12,
     intrinsic_gtol: Optional[float] = 1e-12,
@@ -1787,6 +1849,53 @@ def guarded_two_step_bundle_adjustment(
     if pose_stage_ray_slack < 0:
         raise ValueError("pose_stage_ray_slack must be non-negative")
 
+    normalized_pose_stage_configs = list(pose_stage_configs or [])
+
+    def pose_stage_variants() -> List[Dict[str, object]]:
+        if not normalized_pose_stage_configs:
+            return [
+                {
+                    "prior_sigmas": pose_prior_sigmas,
+                    "parameter_bounds": pose_parameter_bounds,
+                    "max_nfev": pose_max_nfev,
+                    "optimize_points": pose_optimize_points,
+                    "x_scale": pose_x_scale,
+                    "loss": pose_loss,
+                    "method": pose_method,
+                }
+            ]
+
+        variants: List[Dict[str, object]] = []
+        for variant in normalized_pose_stage_configs:
+            variant_dict = dict(variant)
+            variants.append(
+                {
+                    "prior_sigmas": cast(
+                        Optional[Dict[str, float]],
+                        variant_dict.get("prior_sigmas", pose_prior_sigmas),
+                    ),
+                    "parameter_bounds": cast(
+                        Optional[Dict[str, Tuple[float, float]]],
+                        variant_dict.get("parameter_bounds", pose_parameter_bounds),
+                    ),
+                    "max_nfev": cast(
+                        Optional[int],
+                        variant_dict.get("max_nfev", pose_max_nfev),
+                    ),
+                    "optimize_points": cast(
+                        bool,
+                        variant_dict.get("optimize_points", pose_optimize_points),
+                    ),
+                    "x_scale": cast(
+                        Optional[float | Sequence[float] | Dict[str, float]],
+                        variant_dict.get("x_scale", pose_x_scale),
+                    ),
+                    "loss": cast(str, variant_dict.get("loss", pose_loss)),
+                    "method": cast(str, variant_dict.get("method", pose_method)),
+                }
+            )
+        return variants
+
     def projection_drift_summaries(
         reference_cals: List[Calibration],
         candidate_cals: List[Calibration],
@@ -1968,54 +2077,129 @@ def correspondence_stage_ok(
     )
 
     pose_stage_summaries: List[Dict[str, object]] = []
+    pose_stage_variants_list = pose_stage_variants()
     pose_result: object
     pose_ok: bool
     if pose_release_camera_order is None:
-        pose_cals, pose_points, pose_result = multi_camera_bundle_adjustment(
-            observed_pixels,
-            base_cals,
-            cpar,
-            pose_orient_par,
-            point_init=base_points,
-            fixed_camera_indices=fixed_camera_indices,
-            loss=pose_loss,
-            method=pose_method,
-            prior_sigmas=pose_prior_sigmas,
-            parameter_bounds=pose_parameter_bounds,
-            max_nfev=pose_max_nfev,
-            optimize_extrinsics=True,
-            optimize_points=pose_optimize_points,
-            known_points=known_points,
-            known_point_sigmas=known_point_sigmas,
-        )
-        pose_rms = reprojection_rms(observed_pixels, pose_points, pose_cals, cpar)
-        pose_ray_convergence = mean_ray_convergence(observed_pixels, pose_cals, cpar)
-        pose_geometry = projection_drift_summaries(
-            geometry_reference_cals,
-            pose_cals,
-            geometry_reference_points,
-        )
-        pose_geometry_max = max_projection_drift(pose_geometry)
-        pose_correspondence = correspondence_replacement_summary(
-            pose_points,
-            pose_cals,
-        )
-        pose_correspondence_rate = (
-            None
-            if pose_correspondence is None
-            else cast(float, pose_correspondence["replacement_rate"])
-        )
-        pose_ok = pose_rms <= baseline_rms and (
-            not reject_on_ray_convergence
-            or pose_ray_convergence <= baseline_ray_convergence
-        )
-        pose_geometry_ok = geometry_stage_ok(pose_geometry_max, baseline_geometry_max)
-        pose_ok = pose_ok and pose_geometry_ok
-        pose_correspondence_ok = correspondence_stage_ok(
-            pose_correspondence_rate,
-            baseline_correspondence_rate,
-        )
-        pose_ok = pose_ok and pose_correspondence_ok
+        stage_cals = [_clone_calibration(cal) for cal in base_cals]
+        stage_points = np.asarray(base_points, dtype=np.float64).copy()
+        stage_rms = baseline_rms
+        stage_ray_convergence = baseline_ray_convergence
+        stage_geometry = baseline_geometry
+        stage_geometry_max = baseline_geometry_max
+        stage_correspondence = baseline_correspondence
+        stage_correspondence_rate = baseline_correspondence_rate
+        pose_result = {"staged": bool(normalized_pose_stage_configs), "stages": []}
+        pose_ok = False
+        pose_geometry_ok = True
+        pose_correspondence_ok = True
+
+        for micro_stage_index, variant in enumerate(pose_stage_variants_list, start=1):
+            candidate_cals, candidate_points, candidate_result = multi_camera_bundle_adjustment(
+                observed_pixels,
+                stage_cals,
+                cpar,
+                pose_orient_par,
+                point_init=stage_points,
+                fixed_camera_indices=fixed_camera_indices,
+                loss=cast(str, variant["loss"]),
+                method=cast(str, variant["method"]),
+                prior_sigmas=cast(Optional[Dict[str, float]], variant["prior_sigmas"]),
+                parameter_bounds=cast(
+                    Optional[Dict[str, Tuple[float, float]]],
+                    variant["parameter_bounds"],
+                ),
+                max_nfev=cast(Optional[int], variant["max_nfev"]),
+                optimize_extrinsics=True,
+                optimize_points=cast(bool, variant["optimize_points"]),
+                known_points=known_points,
+                known_point_sigmas=known_point_sigmas,
+                x_scale=cast(
+                    Optional[float | Sequence[float] | Dict[str, float]],
+                    variant["x_scale"],
+                ),
+            )
+            candidate_rms = reprojection_rms(
+                observed_pixels, candidate_points, candidate_cals, cpar
+            )
+            candidate_ray_convergence = mean_ray_convergence(
+                observed_pixels, candidate_cals, cpar
+            )
+            candidate_geometry = projection_drift_summaries(
+                geometry_reference_cals,
+                candidate_cals,
+                geometry_reference_points,
+            )
+            candidate_geometry_max = max_projection_drift(candidate_geometry)
+            candidate_correspondence = correspondence_replacement_summary(
+                candidate_points,
+                candidate_cals,
+            )
+            candidate_correspondence_rate = (
+                None
+                if candidate_correspondence is None
+                else cast(float, candidate_correspondence["replacement_rate"])
+            )
+            pose_geometry_ok = geometry_stage_ok(
+                candidate_geometry_max,
+                stage_geometry_max,
+            )
+            pose_correspondence_ok = correspondence_stage_ok(
+                candidate_correspondence_rate,
+                stage_correspondence_rate,
+            )
+            pose_ok = candidate_rms <= stage_rms and (
+                not reject_on_ray_convergence
+                or candidate_ray_convergence <= stage_ray_convergence + pose_stage_ray_slack
+            )
+            pose_ok = pose_ok and pose_geometry_ok and pose_correspondence_ok
+            pose_stage_summaries.append(
+                {
+                    "stage_index": 1,
+                    "micro_stage_index": micro_stage_index,
+                    "released_camera_index": None,
+                    "free_camera_indices": [
+                        camera_index
+                        for camera_index in range(len(cals))
+                        if camera_index not in (fixed_camera_indices or [])
+                    ],
+                    "fixed_camera_indices": fixed_camera_indices,
+                    "reprojection_rms": candidate_rms,
+                    "mean_ray_convergence": candidate_ray_convergence,
+                    "geometry": candidate_geometry,
+                    "geometry_max": candidate_geometry_max,
+                    "geometry_ok": pose_geometry_ok,
+                    "correspondence": candidate_correspondence,
+                    "correspondence_rate": candidate_correspondence_rate,
+                    "correspondence_ok": pose_correspondence_ok,
+                    "accepted": pose_ok or not reject_worse_solution,
+                    "optimize_points": cast(bool, variant["optimize_points"]),
+                    "x_scale": variant["x_scale"],
+                    "result": candidate_result,
+                }
+            )
+            cast(List[object], pose_result["stages"]).append(candidate_result)
+
+            if reject_worse_solution and not pose_ok:
+                break
+
+            stage_cals = candidate_cals
+            stage_points = candidate_points
+            stage_rms = candidate_rms
+            stage_ray_convergence = candidate_ray_convergence
+            stage_geometry = candidate_geometry
+            stage_geometry_max = candidate_geometry_max
+            stage_correspondence = candidate_correspondence
+            stage_correspondence_rate = candidate_correspondence_rate
+
+        pose_cals = stage_cals
+        pose_points = stage_points
+        pose_rms = stage_rms
+        pose_ray_convergence = stage_ray_convergence
+        pose_geometry = stage_geometry
+        pose_geometry_max = stage_geometry_max
+        pose_correspondence = stage_correspondence
+        pose_correspondence_rate = stage_correspondence_rate
     else:
         release_order = [
             int(camera_index) for camera_index in pose_release_camera_order
@@ -2053,93 +2237,114 @@ def correspondence_stage_ok(
                 for camera_index in range(len(cals))
                 if camera_index not in released_cameras
             ]
-            stage_cals, stage_points, stage_result = multi_camera_bundle_adjustment(
-                observed_pixels,
-                current_cals,
-                cpar,
-                pose_orient_par,
-                point_init=current_points,
-                fixed_camera_indices=stage_fixed,
-                loss=pose_loss,
-                method=pose_method,
-                prior_sigmas=pose_prior_sigmas,
-                parameter_bounds=pose_parameter_bounds,
-                max_nfev=pose_max_nfev,
-                optimize_extrinsics=True,
-                optimize_points=pose_optimize_points,
-                known_points=known_points,
-                known_point_sigmas=known_point_sigmas,
-            )
-            stage_rms = reprojection_rms(
-                observed_pixels, stage_points, stage_cals, cpar
-            )
-            stage_ray_convergence = mean_ray_convergence(
-                observed_pixels, stage_cals, cpar
-            )
-            stage_geometry = projection_drift_summaries(
-                geometry_reference_cals,
-                stage_cals,
-                geometry_reference_points,
-            )
-            stage_geometry_max = max_projection_drift(stage_geometry)
-            stage_correspondence = correspondence_replacement_summary(
-                stage_points,
-                stage_cals,
-            )
-            stage_correspondence_rate = (
-                None
-                if stage_correspondence is None
-                else cast(float, stage_correspondence["replacement_rate"])
-            )
-            stage_geometry_ok = geometry_stage_ok(
-                stage_geometry_max,
-                current_geometry_max,
-            )
-            stage_correspondence_ok = correspondence_stage_ok(
-                stage_correspondence_rate,
-                current_correspondence_rate,
-            )
-            stage_ok = stage_rms <= current_rms and (
-                not reject_on_ray_convergence
-                or stage_ray_convergence
-                <= current_ray_convergence + pose_stage_ray_slack
-            )
-            stage_ok = stage_ok and stage_geometry_ok and stage_correspondence_ok
-            pose_stage_summaries.append(
-                {
-                    "stage_index": stage_index,
-                    "released_camera_index": released_camera,
-                    "free_camera_indices": released_cameras.copy(),
-                    "fixed_camera_indices": stage_fixed,
-                    "reprojection_rms": stage_rms,
-                    "mean_ray_convergence": stage_ray_convergence,
-                    "geometry": stage_geometry,
-                    "geometry_max": stage_geometry_max,
-                    "geometry_ok": stage_geometry_ok,
-                    "correspondence": stage_correspondence,
-                    "correspondence_rate": stage_correspondence_rate,
-                    "correspondence_ok": stage_correspondence_ok,
-                    "accepted": stage_ok or not reject_worse_solution,
-                    "result": stage_result,
-                }
-            )
-            cast(List[object], pose_result["stages"]).append(stage_result)
+            stage_ok = False
+            for micro_stage_index, variant in enumerate(
+                pose_stage_variants_list,
+                start=1,
+            ):
+                stage_cals, stage_points, stage_result = multi_camera_bundle_adjustment(
+                    observed_pixels,
+                    current_cals,
+                    cpar,
+                    pose_orient_par,
+                    point_init=current_points,
+                    fixed_camera_indices=stage_fixed,
+                    loss=cast(str, variant["loss"]),
+                    method=cast(str, variant["method"]),
+                    prior_sigmas=cast(
+                        Optional[Dict[str, float]],
+                        variant["prior_sigmas"],
+                    ),
+                    parameter_bounds=cast(
+                        Optional[Dict[str, Tuple[float, float]]],
+                        variant["parameter_bounds"],
+                    ),
+                    max_nfev=cast(Optional[int], variant["max_nfev"]),
+                    optimize_extrinsics=True,
+                    optimize_points=cast(bool, variant["optimize_points"]),
+                    known_points=known_points,
+                    known_point_sigmas=known_point_sigmas,
+                    x_scale=cast(
+                        Optional[float | Sequence[float] | Dict[str, float]],
+                        variant["x_scale"],
+                    ),
+                )
+                stage_rms = reprojection_rms(
+                    observed_pixels, stage_points, stage_cals, cpar
+                )
+                stage_ray_convergence = mean_ray_convergence(
+                    observed_pixels, stage_cals, cpar
+                )
+                stage_geometry = projection_drift_summaries(
+                    geometry_reference_cals,
+                    stage_cals,
+                    geometry_reference_points,
+                )
+                stage_geometry_max = max_projection_drift(stage_geometry)
+                stage_correspondence = correspondence_replacement_summary(
+                    stage_points,
+                    stage_cals,
+                )
+                stage_correspondence_rate = (
+                    None
+                    if stage_correspondence is None
+                    else cast(float, stage_correspondence["replacement_rate"])
+                )
+                stage_geometry_ok = geometry_stage_ok(
+                    stage_geometry_max,
+                    current_geometry_max,
+                )
+                stage_correspondence_ok = correspondence_stage_ok(
+                    stage_correspondence_rate,
+                    current_correspondence_rate,
+                )
+                stage_ok = stage_rms <= current_rms and (
+                    not reject_on_ray_convergence
+                    or stage_ray_convergence
+                    <= current_ray_convergence + pose_stage_ray_slack
+                )
+                stage_ok = stage_ok and stage_geometry_ok and stage_correspondence_ok
+                pose_stage_summaries.append(
+                    {
+                        "stage_index": stage_index,
+                        "micro_stage_index": micro_stage_index,
+                        "released_camera_index": released_camera,
+                        "free_camera_indices": released_cameras.copy(),
+                        "fixed_camera_indices": stage_fixed,
+                        "reprojection_rms": stage_rms,
+                        "mean_ray_convergence": stage_ray_convergence,
+                        "geometry": stage_geometry,
+                        "geometry_max": stage_geometry_max,
+                        "geometry_ok": stage_geometry_ok,
+                        "correspondence": stage_correspondence,
+                        "correspondence_rate": stage_correspondence_rate,
+                        "correspondence_ok": stage_correspondence_ok,
+                        "accepted": stage_ok or not reject_worse_solution,
+                        "optimize_points": cast(bool, variant["optimize_points"]),
+                        "x_scale": variant["x_scale"],
+                        "result": stage_result,
+                    }
+                )
+                cast(List[object], pose_result["stages"]).append(stage_result)
+
+                if reject_worse_solution and not stage_ok:
+                    break
+
+                current_cals = stage_cals
+                current_points = stage_points
+                current_rms = stage_rms
+                current_ray_convergence = stage_ray_convergence
+                current_geometry = stage_geometry
+                current_geometry_max = stage_geometry_max
+                current_correspondence = stage_correspondence
+                current_correspondence_rate = stage_correspondence_rate
+                pose_ok = True
+                pose_geometry_ok = stage_geometry_ok
+                pose_correspondence_ok = stage_correspondence_ok
 
             if reject_worse_solution and not stage_ok:
                 break
 
-            current_cals = stage_cals
-            current_points = stage_points
-            current_rms = stage_rms
-            current_ray_convergence = stage_ray_convergence
-            current_geometry = stage_geometry
-            current_geometry_max = stage_geometry_max
-            current_correspondence = stage_correspondence
-            current_correspondence_rate = stage_correspondence_rate
-            pose_ok = True
-            pose_geometry_ok = stage_geometry_ok
-            pose_correspondence_ok = stage_correspondence_ok
-
         pose_cals = current_cals
         pose_points = current_points
         pose_rms = current_rms
@@ -2166,6 +2371,7 @@ def correspondence_stage_ok(
         optimize_points=intrinsic_optimize_points,
         known_points=known_points,
         known_point_sigmas=known_point_sigmas,
+        x_scale=intrinsic_x_scale,
         ftol=intrinsic_ftol,
         xtol=intrinsic_xtol,
         gtol=intrinsic_gtol,
@@ -2244,6 +2450,7 @@ def correspondence_stage_ok(
         "pose_geometry_ok": pose_geometry_ok,
         "pose_release_camera_order": pose_release_camera_order,
         "pose_stage_ray_slack": pose_stage_ray_slack,
+        "pose_stage_configs": normalized_pose_stage_configs,
         "pose_stage_summaries": pose_stage_summaries,
         "accepted_pose_stage_count": len(
             [summary for summary in pose_stage_summaries if summary["accepted"]]
diff --git a/tests/test_bundle_adjustment.py b/tests/test_bundle_adjustment.py
index 4a72c78..fd2470f 100644
--- a/tests/test_bundle_adjustment.py
+++ b/tests/test_bundle_adjustment.py
@@ -3,6 +3,7 @@
 from unittest.mock import patch
 
 import numpy as np
+import scipy.optimize
 
 from openptv_python.calibration import Calibration, read_calibration
 from openptv_python.imgcoord import image_coordinates
@@ -144,6 +145,63 @@ def test_multi_camera_bundle_adjustment_improves_synthetic_reprojection(self):
         self.assertLess(after_rms, before_rms * 0.4)
         self.assertLess(after_rms, 3e-2)
 
+    def test_multi_camera_bundle_adjustment_passes_x_scale_to_least_squares(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        start_cals = self.lightly_perturb_calibrations(self.true_cals)
+
+        def fake_least_squares(_fun, x0, **kwargs):
+            self.assertIn("x_scale", kwargs)
+            np.testing.assert_allclose(
+                kwargs["x_scale"],
+                np.array([0.02, 0.02, 0.02, 2e-4, 2e-4, 2e-4]),
+            )
+            return scipy.optimize.OptimizeResult(
+                x=x0,
+                success=True,
+                message="ok",
+            )
+
+        with patch(
+            "openptv_python.orientation.scipy.optimize.least_squares",
+            side_effect=fake_least_squares,
+        ):
+            _, refined_points, result = multi_camera_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                point_init=points.copy(),
+                fixed_camera_indices=[0, 1, 2],
+                optimize_points=False,
+                loss="linear",
+                method="trf",
+                x_scale={
+                    "x0": 0.02,
+                    "y0": 0.02,
+                    "z0": 0.02,
+                    "omega": 2e-4,
+                    "phi": 2e-4,
+                    "kappa": 2e-4,
+                },
+            )
+
+        self.assertTrue(result.success)
+        np.testing.assert_allclose(refined_points, points)
+
     def test_cavity_reprojection_improves(self):
         cavity_dir = Path("tests/testing_fodder/test_cavity")
         control = ControlPar(4).from_file(cavity_dir / "parameters/ptv.par")
@@ -735,6 +793,123 @@ def test_guarded_two_step_bundle_adjustment_supports_staged_camera_release(self)
         )
         self.assertEqual(final_points.shape, points.shape)
 
+    def test_guarded_two_step_bundle_adjustment_supports_pose_micro_stages(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        start_cals = self.lightly_perturb_calibrations(self.true_cals)
+        intrinsics = OrientPar()
+        intrinsics.k1flag = 1
+        intrinsics.p1flag = 1
+        intrinsics.p2flag = 1
+
+        staged_returns = [
+            (self.true_cals, points.copy(), {"success": True, "stage": f"pose_{idx}"})
+            for idx in range(4)
+        ] + [(self.true_cals, points.copy(), {"success": True, "stage": "intrinsics"})]
+
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=staged_returns,
+            ) as mocked_adjustment,
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 9.5, 9.0, 8.5, 8.0, 7.5],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[6.0, 5.5, 5.0, 4.5, 4.0, 3.5],
+            ),
+        ):
+            _, _, summary = guarded_two_step_bundle_adjustment(
+                observed_pixels,
+                start_cals,
+                self.control,
+                OrientPar(),
+                intrinsics,
+                point_init=points,
+                pose_release_camera_order=[0, 1],
+                pose_stage_configs=[
+                    {
+                        "optimize_points": False,
+                        "max_nfev": 3,
+                        "x_scale": {
+                            "x0": 0.02,
+                            "y0": 0.02,
+                            "z0": 0.02,
+                            "omega": 2e-4,
+                            "phi": 2e-4,
+                            "kappa": 2e-4,
+                        },
+                    },
+                    {
+                        "optimize_points": True,
+                        "max_nfev": 4,
+                        "x_scale": {
+                            "x0": 0.05,
+                            "y0": 0.05,
+                            "z0": 0.05,
+                            "omega": 5e-4,
+                            "phi": 5e-4,
+                            "kappa": 5e-4,
+                            "points": 0.1,
+                        },
+                    },
+                ],
+                intrinsic_max_nfev=5,
+            )
+
+        self.assertEqual(mocked_adjustment.call_count, 5)
+        fixed_sequences = [
+            call.kwargs.get("fixed_camera_indices")
+            for call in mocked_adjustment.call_args_list
+        ]
+        self.assertEqual(
+            fixed_sequences,
+            [[1, 2, 3], [1, 2, 3], [2, 3], [2, 3], [0, 1, 2, 3]],
+        )
+        self.assertEqual(
+            [
+                call.kwargs.get("optimize_points")
+                for call in mocked_adjustment.call_args_list[:-1]
+            ],
+            [False, True, False, True],
+        )
+        self.assertEqual(
+            mocked_adjustment.call_args_list[0].kwargs.get("x_scale"),
+            {
+                "x0": 0.02,
+                "y0": 0.02,
+                "z0": 0.02,
+                "omega": 2e-4,
+                "phi": 2e-4,
+                "kappa": 2e-4,
+            },
+        )
+        self.assertEqual(summary["accepted_pose_stage_count"], 4)
+        self.assertEqual(len(summary["pose_stage_summaries"]), 4)
+        self.assertEqual(
+            [stage["micro_stage_index"] for stage in summary["pose_stage_summaries"]],
+            [1, 2, 1, 2],
+        )
+        self.assertEqual(
+            [stage["optimize_points"] for stage in summary["pose_stage_summaries"]],
+            [False, True, False, True],
+        )
+
     def test_guarded_two_step_bundle_adjustment_stagewise_ray_slack_allows_near_miss(
         self,
     ):
diff --git a/tests/test_demo_bundle_adjustment.py b/tests/test_demo_bundle_adjustment.py
index c68978f..63637af 100644
--- a/tests/test_demo_bundle_adjustment.py
+++ b/tests/test_demo_bundle_adjustment.py
@@ -1,10 +1,12 @@
 import unittest
 from pathlib import Path
+from unittest.mock import patch
 
 import numpy as np
 
 from openptv_python.calibration import read_calibration
 from openptv_python.demo_bundle_adjustment import (
+    ExperimentSpec,
     ExperimentResult,
     all_fixed_camera_pairs,
     build_experiment_start_calibrations,
@@ -16,12 +18,15 @@
     load_reference_geometry_points,
     normalize_staged_release_order,
     perturb_calibrations,
+    run_experiment,
     summarize_epipolar_consistency,
     summarize_fixed_camera_diagnostics,
     summarize_quadruplet_sensitivity,
 )
+from openptv_python.imgcoord import image_coordinates
 from openptv_python.parameters import ControlPar, read_volume_par
 from openptv_python.tracking_frame_buf import read_path_frame, read_targets
+from openptv_python.trafo import arr_metric_to_pixel
 
 
 class TestBundleAdjustmentDemo(unittest.TestCase):
@@ -47,6 +52,7 @@ def test_default_experiments_adds_known_point_presets(self):
 
         names = [spec.name for spec in experiments]
         self.assertIn("intrinsics_only", names)
+        self.assertIn("intrinsics_first_guarded_stagewise_release", names)
         self.assertIn("pose_trf_known_points", names)
         self.assertIn("guarded_two_step_known_points", names)
         self.assertIn("guarded_stagewise_release", names)
@@ -71,6 +77,11 @@ def test_default_experiments_adds_known_point_presets(self):
         guarded_spec = next(
             spec for spec in experiments if spec.name == "guarded_two_step_known_points"
         )
+        intrinsics_first_spec = next(
+            spec
+            for spec in experiments
+            if spec.name == "intrinsics_first_guarded_stagewise_release"
+        )
         staged_spec = next(
             spec for spec in experiments if spec.name == "guarded_stagewise_release"
         )
@@ -88,14 +99,115 @@ def test_default_experiments_adds_known_point_presets(self):
             staged_spec.ba_kwargs["pose_release_camera_order"], [0, 1, 2, 3]
         )
         self.assertEqual(staged_spec.ba_kwargs["pose_stage_ray_slack"], 0.0)
+        self.assertEqual(len(staged_spec.ba_kwargs["pose_stage_configs"]), 3)
+        self.assertFalse(staged_spec.ba_kwargs["pose_stage_configs"][0]["optimize_points"])
+        self.assertTrue(staged_spec.ba_kwargs["pose_stage_configs"][1]["optimize_points"])
+        self.assertTrue(staged_spec.ba_kwargs["pose_stage_configs"][2]["optimize_points"])
+        self.assertEqual(intrinsics_first_spec.mode, "intrinsics_then_guarded")
+        self.assertFalse(intrinsics_first_spec.ba_kwargs["warmstart_optimize_extrinsics"])
+        self.assertFalse(intrinsics_first_spec.ba_kwargs["warmstart_optimize_points"])
+        self.assertEqual(
+            intrinsics_first_spec.ba_kwargs["pose_release_camera_order"],
+            [0, 1, 2, 3],
+        )
+        self.assertEqual(
+            len(intrinsics_first_spec.ba_kwargs["pose_stage_configs"]),
+            2,
+        )
+        self.assertFalse(
+            intrinsics_first_spec.ba_kwargs["pose_stage_configs"][0]["optimize_points"]
+        )
+        self.assertTrue(
+            intrinsics_first_spec.ba_kwargs["pose_stage_configs"][1]["optimize_points"]
+        )
         self.assertIs(staged_known_spec.ba_kwargs["known_points"], known_points)
         self.assertEqual(staged_known_spec.ba_kwargs["known_point_sigmas"], 0.25)
+        self.assertTrue(
+            all(
+                stage_config["optimize_points"]
+                for stage_config in guarded_spec.ba_kwargs["pose_stage_configs"]
+            )
+        )
+        self.assertTrue(
+            all(
+                stage_config["optimize_points"]
+                for stage_config in staged_known_spec.ba_kwargs["pose_stage_configs"]
+            )
+        )
         self.assertEqual(guarded_spec.ba_kwargs["geometry_guard_mode"], "off")
         self.assertIsNone(guarded_spec.ba_kwargs["geometry_guard_threshold"])
         self.assertEqual(guarded_spec.ba_kwargs["correspondence_guard_mode"], "off")
         self.assertIsNone(guarded_spec.ba_kwargs["correspondence_guard_threshold"])
         self.assertIsNone(guarded_spec.ba_kwargs["correspondence_guard_reference_rate"])
 
+    def test_run_experiment_intrinsics_then_guarded_executes_warmstart_first(self):
+        control = ControlPar(4).from_file(
+            Path("tests/testing_folder/control_parameters/control.par")
+        )
+        add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
+        reference_cals = [
+            read_calibration(
+                Path(f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"),
+                add_file,
+            )
+            for cam_num in range(1, 5)
+        ]
+        start_cals = perturb_calibrations(reference_cals, 1.0)
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(reference_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, control.mm),
+                control,
+            )
+
+        spec = next(
+            experiment
+            for experiment in default_experiments(perturbation_scale=1.0)
+            if experiment.name == "intrinsics_first_guarded_stagewise_release"
+        )
+
+        with (
+            patch(
+                "openptv_python.demo_bundle_adjustment.multi_camera_bundle_adjustment",
+                return_value=(reference_cals, points.copy(), {"success": True, "final_reprojection_rms": 3.8, "message": "warm"}),
+            ) as warmstart,
+            patch(
+                "openptv_python.demo_bundle_adjustment.guarded_two_step_bundle_adjustment",
+                return_value=(reference_cals, points.copy(), {"accepted_stage": "intrinsics", "final_reprojection_rms": 3.7, "final_mean_ray_convergence": 0.2}),
+            ) as guarded,
+        ):
+            result = run_experiment(
+                spec,
+                observed_pixels=observed_pixels,
+                point_init=points,
+                control=control,
+                start_cals=start_cals,
+                reference_cals=reference_cals,
+                reference_geometry_points=None,
+                tracking_data=None,
+                geometry_export_threshold=None,
+                correspondence_export_threshold=None,
+                source_case_dir=Path("tests/testing_folder"),
+                output_dir=None,
+            )
+
+        self.assertEqual(warmstart.call_count, 1)
+        self.assertEqual(guarded.call_count, 1)
+        self.assertFalse(warmstart.call_args.kwargs["optimize_extrinsics"])
+        self.assertFalse(warmstart.call_args.kwargs["optimize_points"])
+        np.testing.assert_allclose(guarded.call_args.kwargs["point_init"], points)
+        self.assertIn("warmstart_rms=3.800000", result.notes)
+        self.assertIn("accepted_stage=intrinsics", result.notes)
+
     def test_default_experiments_accepts_geometry_guard_configuration(self):
         experiments = default_experiments(
             num_cams=4,

From 5bdcdb4703c987a6df6f9115bf5a0b24eda76b39 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 13:49:31 +0200
Subject: [PATCH 12/27] additonal block wise alternative bundle adjustment

---
 openptv_python/demo_bundle_adjustment.py | 158 ++++++
 openptv_python/orientation.py            | 675 ++++++++++++++++++++++-
 tests/test_bundle_adjustment.py          |  90 +++
 tests/test_demo_bundle_adjustment.py     |  84 +++
 4 files changed, 1006 insertions(+), 1 deletion(-)

diff --git a/openptv_python/demo_bundle_adjustment.py b/openptv_python/demo_bundle_adjustment.py
index 5eac602..778be24 100644
--- a/openptv_python/demo_bundle_adjustment.py
+++ b/openptv_python/demo_bundle_adjustment.py
@@ -19,6 +19,7 @@
 from .epi import epipolar_curve
 from .imgcoord import image_coordinates, img_coord
 from .orientation import (
+    alternating_bundle_adjustment,
     guarded_two_step_bundle_adjustment,
     initialize_bundle_adjustment_points,
     mean_ray_convergence,
@@ -1361,6 +1362,42 @@ def default_experiments(
                 "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
             },
         ),
+        ExperimentSpec(
+            name="intrinsics_first_alternating_stagewise_release",
+            description=(
+                "Intrinsics-only warm start followed by alternating "
+                "point/rotation/translation guarded updates"
+            ),
+            mode="alternating",
+            ba_kwargs={
+                "pose_orient_par": OrientPar(),
+                "intrinsic_orient_par": intrinsics,
+                "fixed_camera_indices": staged_fixed,
+                "pose_release_camera_order": staged_order,
+                "pose_stage_ray_slack": pose_stage_ray_slack,
+                "pose_prior_sigmas": pose_priors,
+                "pose_parameter_bounds": pose_bounds,
+                "pose_max_nfev": 4,
+                "pose_x_scale": {
+                    "x0": 0.02,
+                    "y0": 0.02,
+                    "z0": 0.02,
+                    "omega": 2e-4,
+                    "phi": 2e-4,
+                    "kappa": 2e-4,
+                    "points": 0.1,
+                },
+                "pose_block_configs": intrinsics_first_pose_stage_configs,
+                "intrinsic_prior_sigmas": tight_intrinsic_priors,
+                "intrinsic_parameter_bounds": tight_intrinsic_bounds,
+                "intrinsic_max_nfev": 4,
+                "geometry_guard_mode": geometry_guard_mode,
+                "geometry_guard_threshold": geometry_guard_threshold,
+                "correspondence_guard_mode": correspondence_guard_mode,
+                "correspondence_guard_threshold": correspondence_guard_threshold,
+                "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
+            },
+        ),
     ]
 
     if known_points:
@@ -1869,6 +1906,127 @@ def run_experiment(
             f"warmstart_rms={warmstart_rms:.6f}; "
             f"accepted_stage={summary['accepted_stage']}"
         )
+    elif spec.mode == "alternating":
+        pose_orient_par = cast(OrientPar, spec.ba_kwargs["pose_orient_par"])
+        intrinsic_orient_par = cast(OrientPar, spec.ba_kwargs["intrinsic_orient_par"])
+        refined_cals, refined_points, summary = alternating_bundle_adjustment(
+            observed_pixels,
+            working_cals,
+            control,
+            pose_orient_par,
+            intrinsic_orient_par,
+            point_init=point_init,
+            fixed_camera_indices=cast(
+                List[int] | None,
+                spec.ba_kwargs.get("fixed_camera_indices"),
+            ),
+            pose_release_camera_order=cast(
+                List[int] | None,
+                spec.ba_kwargs.get("pose_release_camera_order"),
+            ),
+            pose_stage_ray_slack=cast(
+                float,
+                spec.ba_kwargs.get("pose_stage_ray_slack", 0.0),
+            ),
+            pose_prior_sigmas=cast(
+                Dict[str, float] | None,
+                spec.ba_kwargs.get("pose_prior_sigmas"),
+            ),
+            pose_parameter_bounds=cast(
+                Dict[str, tuple[float, float]] | None,
+                spec.ba_kwargs.get("pose_parameter_bounds"),
+            ),
+            pose_loss=cast(str, spec.ba_kwargs.get("pose_loss", "linear")),
+            pose_method=cast(str, spec.ba_kwargs.get("pose_method", "trf")),
+            pose_max_nfev=cast(int | None, spec.ba_kwargs.get("pose_max_nfev")),
+            pose_x_scale=cast(
+                float | Sequence[float] | Dict[str, float] | None,
+                spec.ba_kwargs.get("pose_x_scale"),
+            ),
+            pose_block_configs=cast(
+                Sequence[Dict[str, object]] | None,
+                spec.ba_kwargs.get("pose_block_configs"),
+            ),
+            intrinsic_prior_sigmas=cast(
+                Dict[str, float] | None,
+                spec.ba_kwargs.get("intrinsic_prior_sigmas"),
+            ),
+            intrinsic_parameter_bounds=cast(
+                Dict[str, tuple[float, float]] | None,
+                spec.ba_kwargs.get("intrinsic_parameter_bounds"),
+            ),
+            intrinsic_loss=cast(
+                str,
+                spec.ba_kwargs.get("intrinsic_loss", "linear"),
+            ),
+            intrinsic_method=cast(
+                str,
+                spec.ba_kwargs.get("intrinsic_method", "trf"),
+            ),
+            intrinsic_max_nfev=cast(
+                int | None,
+                spec.ba_kwargs.get("intrinsic_max_nfev"),
+            ),
+            intrinsic_x_scale=cast(
+                float | Sequence[float] | Dict[str, float] | None,
+                spec.ba_kwargs.get("intrinsic_x_scale"),
+            ),
+            intrinsic_ftol=cast(
+                float | None, spec.ba_kwargs.get("intrinsic_ftol", 1e-12)
+            ),
+            intrinsic_xtol=cast(
+                float | None, spec.ba_kwargs.get("intrinsic_xtol", 1e-12)
+            ),
+            intrinsic_gtol=cast(
+                float | None, spec.ba_kwargs.get("intrinsic_gtol", 1e-12)
+            ),
+            known_points=cast(
+                Dict[int, np.ndarray] | None,
+                spec.ba_kwargs.get("known_points"),
+            ),
+            known_point_sigmas=cast(
+                float | np.ndarray | None,
+                spec.ba_kwargs.get("known_point_sigmas"),
+            ),
+            geometry_reference_points=reference_geometry_points,
+            geometry_reference_cals=reference_cals,
+            geometry_guard_mode=cast(
+                str,
+                spec.ba_kwargs.get("geometry_guard_mode", "off"),
+            ),
+            geometry_guard_threshold=cast(
+                float | None,
+                spec.ba_kwargs.get("geometry_guard_threshold"),
+            ),
+            correspondence_original_ids=(
+                None if tracking_data is None else tracking_data.original_target_ids
+            ),
+            correspondence_point_frame_indices=(
+                None if tracking_data is None else tracking_data.point_frame_indices
+            ),
+            correspondence_frame_target_pixels=(
+                None if tracking_data is None else tracking_data.frame_target_pixels
+            ),
+            correspondence_guard_mode=cast(
+                str,
+                spec.ba_kwargs.get("correspondence_guard_mode", "off"),
+            ),
+            correspondence_guard_threshold=cast(
+                float | None,
+                spec.ba_kwargs.get("correspondence_guard_threshold"),
+            ),
+            correspondence_guard_reference_rate=cast(
+                float | None,
+                spec.ba_kwargs.get("correspondence_guard_reference_rate"),
+            ),
+        )
+        success = True
+        final_rms = cast(float, summary["final_reprojection_rms"])
+        final_ray = cast(float, summary["final_mean_ray_convergence"])
+        notes = (
+            f"warmstart_ok={summary['warmstart_ok']}; "
+            f"accepted_stage={summary['accepted_stage']}"
+        )
     else:
         raise ValueError(f"Unknown experiment mode: {spec.mode}")
 
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index 1074deb..b273ce6 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -1568,7 +1568,12 @@ def multi_camera_bundle_adjustment(
     else:
         optimized_cam_indices = list(range(num_cams))
 
-    if not optimize_extrinsics and not optional_names and not include_interface:
+    if (
+        not optimize_extrinsics
+        and not optional_names
+        and not include_interface
+        and not optimize_points
+    ):
         raise ValueError("No camera parameters are enabled for optimization")
 
     if not optimize_points and not optimized_cam_indices:
@@ -2481,3 +2486,671 @@ def correspondence_stage_ok(
     }
 
     return final_cals, final_points, summary
+
+
+def alternating_bundle_adjustment(
+    observed_pixels: np.ndarray,
+    cals: List[Calibration],
+    cpar: ControlPar,
+    pose_orient_par: OrientPar,
+    intrinsic_orient_par: OrientPar,
+    *,
+    point_init: Optional[np.ndarray] = None,
+    fixed_camera_indices: Optional[List[int]] = None,
+    pose_release_camera_order: Optional[List[int]] = None,
+    pose_stage_ray_slack: float = 0.0,
+    pose_prior_sigmas: Optional[Dict[str, float]] = None,
+    pose_parameter_bounds: Optional[Dict[str, Tuple[float, float]]] = None,
+    pose_loss: str = "linear",
+    pose_method: str = "trf",
+    pose_max_nfev: Optional[int] = None,
+    pose_x_scale: Optional[float | Sequence[float] | Dict[str, float]] = None,
+    pose_block_configs: Optional[Sequence[Dict[str, object]]] = None,
+    intrinsic_prior_sigmas: Optional[Dict[str, float]] = None,
+    intrinsic_parameter_bounds: Optional[Dict[str, Tuple[float, float]]] = None,
+    intrinsic_loss: str = "linear",
+    intrinsic_method: str = "trf",
+    intrinsic_max_nfev: Optional[int] = None,
+    intrinsic_x_scale: Optional[float | Sequence[float] | Dict[str, float]] = None,
+    intrinsic_ftol: Optional[float] = 1e-12,
+    intrinsic_xtol: Optional[float] = 1e-12,
+    intrinsic_gtol: Optional[float] = 1e-12,
+    known_points: Optional[Dict[int, np.ndarray]] = None,
+    known_point_sigmas: Optional[float | np.ndarray] = None,
+    geometry_reference_points: Optional[np.ndarray] = None,
+    geometry_reference_cals: Optional[List[Calibration]] = None,
+    geometry_guard_mode: str = "off",
+    geometry_guard_threshold: Optional[float] = None,
+    correspondence_original_ids: Optional[np.ndarray] = None,
+    correspondence_point_frame_indices: Optional[np.ndarray] = None,
+    correspondence_frame_target_pixels: Optional[Sequence[Sequence[np.ndarray]]] = None,
+    correspondence_guard_mode: str = "off",
+    correspondence_guard_threshold: Optional[float] = None,
+    correspondence_guard_reference_rate: Optional[float] = None,
+    reject_worse_solution: bool = True,
+    reject_on_ray_convergence: bool = True,
+) -> Tuple[List[Calibration], np.ndarray, Dict[str, object]]:
+    """Run intrinsics-first alternating BA with point/rotation/translation block updates."""
+    if pose_stage_ray_slack < 0:
+        raise ValueError("pose_stage_ray_slack must be non-negative")
+
+    def merge_bounds(
+        base: Optional[Dict[str, Tuple[float, float]]],
+        updates: Optional[Dict[str, Tuple[float, float]]],
+    ) -> Optional[Dict[str, Tuple[float, float]]]:
+        merged = dict(base or {})
+        if updates is not None:
+            merged.update(updates)
+        return merged or None
+
+    def default_pose_blocks() -> List[Dict[str, object]]:
+        return [
+            {
+                "name": "points_only",
+                "optimize_extrinsics": False,
+                "optimize_points": True,
+                "loss": pose_loss,
+                "method": pose_method,
+                "max_nfev": 4 if pose_max_nfev is None else min(4, pose_max_nfev),
+                "x_scale": {"points": 0.1},
+            },
+            {
+                "name": "rotation_only",
+                "optimize_extrinsics": True,
+                "optimize_points": False,
+                "freeze_translation": True,
+                "loss": pose_loss,
+                "method": pose_method,
+                "max_nfev": 4 if pose_max_nfev is None else min(4, pose_max_nfev),
+                "x_scale": {
+                    "omega": 2e-4,
+                    "phi": 2e-4,
+                    "kappa": 2e-4,
+                },
+            },
+            {
+                "name": "translation_only",
+                "optimize_extrinsics": True,
+                "optimize_points": False,
+                "freeze_rotation": True,
+                "loss": pose_loss,
+                "method": pose_method,
+                "max_nfev": 4 if pose_max_nfev is None else min(4, pose_max_nfev),
+                "x_scale": {
+                    "x0": 0.02,
+                    "y0": 0.02,
+                    "z0": 0.02,
+                },
+            },
+            {
+                "name": "joint_pose_points",
+                "optimize_extrinsics": True,
+                "optimize_points": True,
+                "loss": pose_loss,
+                "method": pose_method,
+                "max_nfev": pose_max_nfev,
+                "x_scale": pose_x_scale,
+            },
+        ]
+
+    normalized_pose_block_configs = list(
+        default_pose_blocks() if pose_block_configs is None else pose_block_configs
+    )
+
+    def projection_drift_summaries(
+        reference_cals: List[Calibration],
+        candidate_cals: List[Calibration],
+        reference_points: Optional[np.ndarray],
+    ) -> Optional[List[Dict[str, float]]]:
+        if reference_points is None:
+            return None
+
+        summaries: List[Dict[str, float]] = []
+        for camera_index, (reference_cal, candidate_cal) in enumerate(
+            zip(reference_cals, candidate_cals),
+            start=1,
+        ):
+            reference_pixels = []
+            candidate_pixels = []
+            for point in reference_points:
+                ref_x, ref_y = img_coord(point, reference_cal, cpar.mm)
+                cand_x, cand_y = img_coord(point, candidate_cal, cpar.mm)
+                reference_pixels.append(metric_to_pixel(ref_x, ref_y, cpar))
+                candidate_pixels.append(metric_to_pixel(cand_x, cand_y, cpar))
+
+            displacement = np.linalg.norm(
+                np.asarray(candidate_pixels) - np.asarray(reference_pixels),
+                axis=1,
+            )
+            summaries.append(
+                {
+                    "camera_index": float(camera_index),
+                    "mean_distance": float(displacement.mean()),
+                    "p95_distance": float(np.percentile(displacement, 95.0)),
+                    "max_distance": float(displacement.max()),
+                }
+            )
+
+        return summaries
+
+    def max_projection_drift(
+        summaries: Optional[List[Dict[str, float]]],
+    ) -> Optional[float]:
+        if not summaries:
+            return None
+        return max(item["max_distance"] for item in summaries)
+
+    def geometry_stage_ok(
+        candidate_metric: Optional[float],
+        baseline_metric: Optional[float],
+    ) -> bool:
+        if geometry_guard_mode == "off" or candidate_metric is None:
+            return True
+        if geometry_guard_mode == "soft":
+            if baseline_metric is None:
+                return True
+            return candidate_metric <= baseline_metric + 1e-12
+        if geometry_guard_mode == "hard":
+            if geometry_guard_threshold is None or geometry_guard_threshold <= 0:
+                raise ValueError(
+                    "geometry_guard_threshold must be positive when geometry_guard_mode='hard'"
+                )
+            return candidate_metric <= geometry_guard_threshold
+        raise ValueError("geometry_guard_mode must be one of 'off', 'soft', or 'hard'")
+
+    def correspondence_replacement_summary(
+        candidate_points: np.ndarray,
+        candidate_cals: List[Calibration],
+    ) -> Optional[Dict[str, object]]:
+        if (
+            correspondence_original_ids is None
+            or correspondence_point_frame_indices is None
+            or correspondence_frame_target_pixels is None
+        ):
+            return None
+
+        projected_pixels = np.empty(
+            (candidate_points.shape[0], len(candidate_cals), 2),
+            dtype=np.float64,
+        )
+        for camera_index, cal in enumerate(candidate_cals):
+            projected_pixels[:, camera_index, :] = arr_metric_to_pixel(
+                image_coordinates(candidate_points, cal, cpar.mm),
+                cpar,
+            )
+
+        replacement_ids = np.empty_like(correspondence_original_ids)
+        nearest_distances = np.empty_like(
+            correspondence_original_ids,
+            dtype=np.float64,
+        )
+        for point_index in range(candidate_points.shape[0]):
+            frame_targets = correspondence_frame_target_pixels[
+                int(correspondence_point_frame_indices[point_index])
+            ]
+            for camera_index in range(len(candidate_cals)):
+                deltas = (
+                    frame_targets[camera_index]
+                    - projected_pixels[point_index, camera_index]
+                )
+                squared_distances = np.sum(deltas * deltas, axis=1)
+                nearest_index = int(np.argmin(squared_distances))
+                replacement_ids[point_index, camera_index] = nearest_index
+                nearest_distances[point_index, camera_index] = float(
+                    np.sqrt(squared_distances[nearest_index])
+                )
+
+        changed_mask = np.any(
+            replacement_ids != correspondence_original_ids,
+            axis=1,
+        )
+        camera_change_rates = [
+            float(
+                np.mean(
+                    replacement_ids[:, camera_index]
+                    != correspondence_original_ids[:, camera_index]
+                )
+            )
+            for camera_index in range(len(candidate_cals))
+        ]
+        return {
+            "replacement_rate": float(np.mean(changed_mask)),
+            "camera_change_rates": camera_change_rates,
+            "mean_nearest_distance": float(np.mean(nearest_distances)),
+            "p95_nearest_distance": float(np.percentile(nearest_distances, 95.0)),
+            "max_nearest_distance": float(np.max(nearest_distances)),
+        }
+
+    def correspondence_stage_ok(
+        candidate_rate: Optional[float],
+        prior_rate: Optional[float],
+    ) -> bool:
+        if correspondence_guard_mode == "off" or candidate_rate is None:
+            return True
+        if correspondence_guard_mode == "soft":
+            if correspondence_guard_reference_rate is not None:
+                return candidate_rate <= correspondence_guard_reference_rate + 1e-12
+            if prior_rate is None:
+                return True
+            return candidate_rate <= prior_rate + 1e-12
+        if correspondence_guard_mode == "hard":
+            if (
+                correspondence_guard_threshold is None
+                or correspondence_guard_threshold <= 0
+            ):
+                raise ValueError(
+                    "correspondence_guard_threshold must be positive when correspondence_guard_mode='hard'"
+                )
+            return candidate_rate <= correspondence_guard_threshold
+        raise ValueError(
+            "correspondence_guard_mode must be one of 'off', 'soft', or 'hard'"
+        )
+
+    base_cals = [_clone_calibration(cal) for cal in cals]
+    if point_init is None:
+        base_points, _ = initialize_bundle_adjustment_points(
+            observed_pixels, base_cals, cpar
+        )
+    else:
+        base_points = np.asarray(point_init, dtype=np.float64)
+        if base_points.shape != (observed_pixels.shape[0], 3):
+            raise ValueError("point_init must have shape (num_points, 3)")
+
+    baseline_rms = reprojection_rms(observed_pixels, base_points, base_cals, cpar)
+    baseline_ray_convergence = mean_ray_convergence(observed_pixels, base_cals, cpar)
+    if geometry_reference_cals is None:
+        geometry_reference_cals = [_clone_calibration(cal) for cal in base_cals]
+
+    baseline_geometry = projection_drift_summaries(
+        geometry_reference_cals,
+        base_cals,
+        geometry_reference_points,
+    )
+    baseline_geometry_max = max_projection_drift(baseline_geometry)
+    baseline_correspondence = correspondence_replacement_summary(
+        base_points,
+        base_cals,
+    )
+    baseline_correspondence_rate = (
+        None
+        if baseline_correspondence is None
+        else cast(float, baseline_correspondence["replacement_rate"])
+    )
+
+    warmstart_fixed = list(range(len(cals)))
+    warmstart_cals, warmstart_points, warmstart_result = multi_camera_bundle_adjustment(
+        observed_pixels,
+        base_cals,
+        cpar,
+        intrinsic_orient_par,
+        point_init=base_points,
+        fixed_camera_indices=warmstart_fixed,
+        loss=intrinsic_loss,
+        method=intrinsic_method,
+        prior_sigmas=intrinsic_prior_sigmas,
+        parameter_bounds=intrinsic_parameter_bounds,
+        max_nfev=intrinsic_max_nfev,
+        optimize_extrinsics=False,
+        optimize_points=False,
+        x_scale=intrinsic_x_scale,
+        ftol=intrinsic_ftol,
+        xtol=intrinsic_xtol,
+        gtol=intrinsic_gtol,
+    )
+    warmstart_rms = reprojection_rms(observed_pixels, warmstart_points, warmstart_cals, cpar)
+    warmstart_ray_convergence = mean_ray_convergence(observed_pixels, warmstart_cals, cpar)
+    warmstart_geometry = projection_drift_summaries(
+        geometry_reference_cals,
+        warmstart_cals,
+        geometry_reference_points,
+    )
+    warmstart_geometry_max = max_projection_drift(warmstart_geometry)
+    warmstart_correspondence = correspondence_replacement_summary(
+        warmstart_points,
+        warmstart_cals,
+    )
+    warmstart_correspondence_rate = (
+        None
+        if warmstart_correspondence is None
+        else cast(float, warmstart_correspondence["replacement_rate"])
+    )
+    warmstart_ok = warmstart_rms <= baseline_rms and (
+        not reject_on_ray_convergence
+        or warmstart_ray_convergence <= baseline_ray_convergence
+    )
+    warmstart_ok = warmstart_ok and geometry_stage_ok(
+        warmstart_geometry_max,
+        baseline_geometry_max,
+    )
+    warmstart_ok = warmstart_ok and correspondence_stage_ok(
+        warmstart_correspondence_rate,
+        baseline_correspondence_rate,
+    )
+
+    if reject_worse_solution and not warmstart_ok:
+        current_cals = [_clone_calibration(cal) for cal in base_cals]
+        current_points = np.asarray(base_points, dtype=np.float64).copy()
+        current_rms = baseline_rms
+        current_ray_convergence = baseline_ray_convergence
+        current_geometry = baseline_geometry
+        current_geometry_max = baseline_geometry_max
+        current_correspondence = baseline_correspondence
+        current_correspondence_rate = baseline_correspondence_rate
+    else:
+        current_cals = warmstart_cals
+        current_points = warmstart_points
+        current_rms = warmstart_rms
+        current_ray_convergence = warmstart_ray_convergence
+        current_geometry = warmstart_geometry
+        current_geometry_max = warmstart_geometry_max
+        current_correspondence = warmstart_correspondence
+        current_correspondence_rate = warmstart_correspondence_rate
+
+    if pose_release_camera_order is None:
+        release_order = [
+            camera_index
+            for camera_index in range(len(cals))
+            if camera_index not in (fixed_camera_indices or [])
+        ]
+    else:
+        release_order = [int(camera_index) for camera_index in pose_release_camera_order]
+    if not release_order:
+        raise ValueError("pose_release_camera_order must not be empty")
+
+    alternating_result: Dict[str, object] = {
+        "warmstart_result": warmstart_result,
+        "stages": [],
+    }
+    block_summaries: List[Dict[str, object]] = []
+    pose_ok = False
+    pose_geometry_ok = True
+    pose_correspondence_ok = True
+    released_cameras: List[int] = []
+
+    for stage_index, released_camera in enumerate(release_order, start=1):
+        released_cameras.append(released_camera)
+        stage_fixed = [
+            camera_index
+            for camera_index in range(len(cals))
+            if camera_index not in released_cameras
+        ]
+        stage_completed = True
+        for block_index, block in enumerate(normalized_pose_block_configs, start=1):
+            block_config = dict(block)
+            optimize_extrinsics = cast(
+                bool,
+                block_config.get("optimize_extrinsics", True),
+            )
+            optimize_points = cast(bool, block_config.get("optimize_points", True))
+            block_bounds = merge_bounds(
+                pose_parameter_bounds,
+                cast(
+                    Optional[Dict[str, Tuple[float, float]]],
+                    block_config.get("parameter_bounds"),
+                ),
+            )
+            if cast(bool, block_config.get("freeze_translation", False)):
+                block_bounds = merge_bounds(
+                    block_bounds,
+                    {"x0": (0.0, 0.0), "y0": (0.0, 0.0), "z0": (0.0, 0.0)},
+                )
+            if cast(bool, block_config.get("freeze_rotation", False)):
+                block_bounds = merge_bounds(
+                    block_bounds,
+                    {
+                        "omega": (0.0, 0.0),
+                        "phi": (0.0, 0.0),
+                        "kappa": (0.0, 0.0),
+                    },
+                )
+
+            block_known_points = known_points if optimize_points else None
+            block_known_point_sigmas = known_point_sigmas if optimize_points else None
+            block_cals, block_points, block_result = multi_camera_bundle_adjustment(
+                observed_pixels,
+                current_cals,
+                cpar,
+                pose_orient_par,
+                point_init=current_points,
+                fixed_camera_indices=stage_fixed,
+                loss=cast(str, block_config.get("loss", pose_loss)),
+                method=cast(str, block_config.get("method", pose_method)),
+                prior_sigmas=cast(
+                    Optional[Dict[str, float]],
+                    block_config.get("prior_sigmas", pose_prior_sigmas),
+                ),
+                parameter_bounds=block_bounds,
+                max_nfev=cast(Optional[int], block_config.get("max_nfev", pose_max_nfev)),
+                optimize_extrinsics=optimize_extrinsics,
+                optimize_points=optimize_points,
+                known_points=block_known_points,
+                known_point_sigmas=block_known_point_sigmas,
+                x_scale=cast(
+                    Optional[float | Sequence[float] | Dict[str, float]],
+                    block_config.get("x_scale", pose_x_scale),
+                ),
+            )
+            block_rms = reprojection_rms(observed_pixels, block_points, block_cals, cpar)
+            block_ray_convergence = mean_ray_convergence(
+                observed_pixels,
+                block_cals,
+                cpar,
+            )
+            block_geometry = projection_drift_summaries(
+                geometry_reference_cals,
+                block_cals,
+                geometry_reference_points,
+            )
+            block_geometry_max = max_projection_drift(block_geometry)
+            block_correspondence = correspondence_replacement_summary(
+                block_points,
+                block_cals,
+            )
+            block_correspondence_rate = (
+                None
+                if block_correspondence is None
+                else cast(float, block_correspondence["replacement_rate"])
+            )
+            block_geometry_ok = geometry_stage_ok(
+                block_geometry_max,
+                current_geometry_max,
+            )
+            block_correspondence_ok = correspondence_stage_ok(
+                block_correspondence_rate,
+                current_correspondence_rate,
+            )
+            block_ok = block_rms <= current_rms and (
+                not reject_on_ray_convergence
+                or block_ray_convergence <= current_ray_convergence + pose_stage_ray_slack
+            )
+            block_ok = block_ok and block_geometry_ok and block_correspondence_ok
+            block_summaries.append(
+                {
+                    "stage_index": stage_index,
+                    "block_index": block_index,
+                    "block_name": block_config.get("name", f"block_{block_index}"),
+                    "released_camera_index": released_camera,
+                    "free_camera_indices": released_cameras.copy(),
+                    "fixed_camera_indices": stage_fixed,
+                    "reprojection_rms": block_rms,
+                    "mean_ray_convergence": block_ray_convergence,
+                    "geometry": block_geometry,
+                    "geometry_max": block_geometry_max,
+                    "geometry_ok": block_geometry_ok,
+                    "correspondence": block_correspondence,
+                    "correspondence_rate": block_correspondence_rate,
+                    "correspondence_ok": block_correspondence_ok,
+                    "accepted": block_ok or not reject_worse_solution,
+                    "optimize_extrinsics": optimize_extrinsics,
+                    "optimize_points": optimize_points,
+                    "result": block_result,
+                }
+            )
+            cast(List[object], alternating_result["stages"]).append(block_result)
+
+            if reject_worse_solution and not block_ok:
+                stage_completed = False
+                break
+
+            current_cals = block_cals
+            current_points = block_points
+            current_rms = block_rms
+            current_ray_convergence = block_ray_convergence
+            current_geometry = block_geometry
+            current_geometry_max = block_geometry_max
+            current_correspondence = block_correspondence
+            current_correspondence_rate = block_correspondence_rate
+            pose_ok = True
+            pose_geometry_ok = block_geometry_ok
+            pose_correspondence_ok = block_correspondence_ok
+
+        if reject_worse_solution and not stage_completed:
+            break
+
+    pose_cals = current_cals
+    pose_points = current_points
+    pose_rms = current_rms
+    pose_ray_convergence = current_ray_convergence
+    pose_geometry = current_geometry
+    pose_geometry_max = current_geometry_max
+    pose_correspondence = current_correspondence
+    pose_correspondence_rate = current_correspondence_rate
+
+    intrinsic_fixed = list(range(len(cals)))
+    intrinsic_cals, intrinsic_points, intrinsic_result = multi_camera_bundle_adjustment(
+        observed_pixels,
+        pose_cals,
+        cpar,
+        intrinsic_orient_par,
+        point_init=pose_points,
+        fixed_camera_indices=intrinsic_fixed,
+        loss=intrinsic_loss,
+        method=intrinsic_method,
+        prior_sigmas=intrinsic_prior_sigmas,
+        parameter_bounds=intrinsic_parameter_bounds,
+        max_nfev=intrinsic_max_nfev,
+        optimize_extrinsics=False,
+        optimize_points=False,
+        x_scale=intrinsic_x_scale,
+        ftol=intrinsic_ftol,
+        xtol=intrinsic_xtol,
+        gtol=intrinsic_gtol,
+    )
+    intrinsic_rms = reprojection_rms(
+        observed_pixels, intrinsic_points, intrinsic_cals, cpar
+    )
+    intrinsic_ray_convergence = mean_ray_convergence(
+        observed_pixels, intrinsic_cals, cpar
+    )
+    intrinsic_geometry = projection_drift_summaries(
+        geometry_reference_cals,
+        intrinsic_cals,
+        geometry_reference_points,
+    )
+    intrinsic_geometry_max = max_projection_drift(intrinsic_geometry)
+    intrinsic_correspondence = correspondence_replacement_summary(
+        intrinsic_points,
+        intrinsic_cals,
+    )
+    intrinsic_correspondence_rate = (
+        None
+        if intrinsic_correspondence is None
+        else cast(float, intrinsic_correspondence["replacement_rate"])
+    )
+
+    accepted_stage = "intrinsics"
+    final_cals = intrinsic_cals
+    final_points = intrinsic_points
+    final_rms = intrinsic_rms
+    final_ray_convergence = intrinsic_ray_convergence
+    intrinsic_ok = intrinsic_rms <= pose_rms and (
+        not reject_on_ray_convergence
+        or intrinsic_ray_convergence <= pose_ray_convergence
+    )
+    intrinsic_ok = intrinsic_ok and geometry_stage_ok(
+        intrinsic_geometry_max,
+        pose_geometry_max,
+    )
+    intrinsic_ok = intrinsic_ok and correspondence_stage_ok(
+        intrinsic_correspondence_rate,
+        pose_correspondence_rate,
+    )
+
+    if reject_worse_solution:
+        if not pose_ok:
+            if warmstart_ok:
+                accepted_stage = "warmstart"
+                final_cals = warmstart_cals
+                final_points = warmstart_points
+                final_rms = warmstart_rms
+                final_ray_convergence = warmstart_ray_convergence
+            else:
+                accepted_stage = "baseline"
+                final_cals = base_cals
+                final_points = base_points
+                final_rms = baseline_rms
+                final_ray_convergence = baseline_ray_convergence
+        elif not intrinsic_ok:
+            accepted_stage = "pose_blocks"
+            final_cals = pose_cals
+            final_points = pose_points
+            final_rms = pose_rms
+            final_ray_convergence = pose_ray_convergence
+
+    summary = {
+        "baseline_reprojection_rms": baseline_rms,
+        "baseline_mean_ray_convergence": baseline_ray_convergence,
+        "baseline_cals": base_cals,
+        "baseline_points": base_points,
+        "baseline_geometry": baseline_geometry,
+        "baseline_geometry_max": baseline_geometry_max,
+        "baseline_correspondence": baseline_correspondence,
+        "baseline_correspondence_rate": baseline_correspondence_rate,
+        "warmstart_reprojection_rms": warmstart_rms,
+        "warmstart_mean_ray_convergence": warmstart_ray_convergence,
+        "warmstart_cals": warmstart_cals,
+        "warmstart_points": warmstart_points,
+        "warmstart_geometry": warmstart_geometry,
+        "warmstart_geometry_max": warmstart_geometry_max,
+        "warmstart_correspondence": warmstart_correspondence,
+        "warmstart_correspondence_rate": warmstart_correspondence_rate,
+        "warmstart_ok": warmstart_ok,
+        "pose_reprojection_rms": pose_rms,
+        "pose_mean_ray_convergence": pose_ray_convergence,
+        "pose_cals": pose_cals,
+        "pose_points": pose_points,
+        "pose_geometry": pose_geometry,
+        "pose_geometry_max": pose_geometry_max,
+        "pose_geometry_ok": pose_geometry_ok,
+        "pose_release_camera_order": release_order,
+        "pose_stage_ray_slack": pose_stage_ray_slack,
+        "pose_block_configs": normalized_pose_block_configs,
+        "pose_block_summaries": block_summaries,
+        "accepted_pose_block_count": len(
+            [block for block in block_summaries if block["accepted"]]
+        ),
+        "pose_correspondence": pose_correspondence,
+        "pose_correspondence_rate": pose_correspondence_rate,
+        "pose_correspondence_ok": pose_correspondence_ok,
+        "intrinsic_reprojection_rms": intrinsic_rms,
+        "intrinsic_mean_ray_convergence": intrinsic_ray_convergence,
+        "intrinsic_cals": intrinsic_cals,
+        "intrinsic_points": intrinsic_points,
+        "intrinsic_geometry": intrinsic_geometry,
+        "intrinsic_geometry_max": intrinsic_geometry_max,
+        "intrinsic_correspondence": intrinsic_correspondence,
+        "intrinsic_correspondence_rate": intrinsic_correspondence_rate,
+        "geometry_guard_mode": geometry_guard_mode,
+        "geometry_guard_threshold": geometry_guard_threshold,
+        "correspondence_guard_mode": correspondence_guard_mode,
+        "correspondence_guard_threshold": correspondence_guard_threshold,
+        "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
+        "accepted_stage": accepted_stage,
+        "final_reprojection_rms": final_rms,
+        "final_mean_ray_convergence": final_ray_convergence,
+        "warmstart_result": warmstart_result,
+        "pose_result": alternating_result,
+        "intrinsic_result": intrinsic_result,
+    }
+
+    return final_cals, final_points, summary
diff --git a/tests/test_bundle_adjustment.py b/tests/test_bundle_adjustment.py
index fd2470f..21cf302 100644
--- a/tests/test_bundle_adjustment.py
+++ b/tests/test_bundle_adjustment.py
@@ -8,6 +8,7 @@
 from openptv_python.calibration import Calibration, read_calibration
 from openptv_python.imgcoord import image_coordinates
 from openptv_python.orientation import (
+    alternating_bundle_adjustment,
     guarded_two_step_bundle_adjustment,
     mean_ray_convergence,
     multi_camera_bundle_adjustment,
@@ -910,6 +911,95 @@ def test_guarded_two_step_bundle_adjustment_supports_pose_micro_stages(self):
             [False, True, False, True],
         )
 
+    def test_alternating_bundle_adjustment_supports_block_schedule(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        staged_returns = [
+            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 9.5}),
+            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 9.0}),
+            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 8.5}),
+            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 8.0}),
+            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 7.5}),
+            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 7.0}),
+        ]
+
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=staged_returns,
+            ) as mocked_adjustment,
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 9.5, 9.0, 8.5, 8.0, 7.5, 7.0],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[6.0, 5.5, 5.0, 4.5, 4.0, 3.5, 3.0],
+            ),
+        ):
+            _, _, summary = alternating_bundle_adjustment(
+                observed_pixels,
+                self.lightly_perturb_calibrations(self.true_cals),
+                self.control,
+                OrientPar(),
+                OrientPar(),
+                point_init=points,
+                pose_release_camera_order=[0, 1],
+                pose_block_configs=[
+                    {
+                        "name": "points_only",
+                        "optimize_extrinsics": False,
+                        "optimize_points": True,
+                        "max_nfev": 3,
+                    },
+                    {
+                        "name": "rotation_only",
+                        "optimize_extrinsics": True,
+                        "optimize_points": False,
+                        "freeze_translation": True,
+                        "max_nfev": 3,
+                    },
+                ],
+                intrinsic_max_nfev=4,
+            )
+
+        self.assertEqual(mocked_adjustment.call_count, 6)
+        fixed_sequences = [
+            call.kwargs.get("fixed_camera_indices")
+            for call in mocked_adjustment.call_args_list
+        ]
+        self.assertEqual(
+            fixed_sequences,
+            [[0, 1, 2, 3], [1, 2, 3], [1, 2, 3], [2, 3], [2, 3], [0, 1, 2, 3]],
+        )
+        self.assertFalse(mocked_adjustment.call_args_list[0].kwargs["optimize_extrinsics"])
+        self.assertFalse(mocked_adjustment.call_args_list[0].kwargs["optimize_points"])
+        self.assertFalse(mocked_adjustment.call_args_list[1].kwargs["optimize_extrinsics"])
+        self.assertTrue(mocked_adjustment.call_args_list[1].kwargs["optimize_points"])
+        self.assertEqual(
+            mocked_adjustment.call_args_list[2].kwargs["parameter_bounds"]["x0"],
+            (0.0, 0.0),
+        )
+        self.assertEqual(summary["accepted_stage"], "intrinsics")
+        self.assertEqual(summary["accepted_pose_block_count"], 4)
+        self.assertEqual(
+            [block["block_name"] for block in summary["pose_block_summaries"]],
+            ["points_only", "rotation_only", "points_only", "rotation_only"],
+        )
+
     def test_guarded_two_step_bundle_adjustment_stagewise_ray_slack_allows_near_miss(
         self,
     ):
diff --git a/tests/test_demo_bundle_adjustment.py b/tests/test_demo_bundle_adjustment.py
index 63637af..8d2a683 100644
--- a/tests/test_demo_bundle_adjustment.py
+++ b/tests/test_demo_bundle_adjustment.py
@@ -53,6 +53,7 @@ def test_default_experiments_adds_known_point_presets(self):
         names = [spec.name for spec in experiments]
         self.assertIn("intrinsics_only", names)
         self.assertIn("intrinsics_first_guarded_stagewise_release", names)
+        self.assertIn("intrinsics_first_alternating_stagewise_release", names)
         self.assertIn("pose_trf_known_points", names)
         self.assertIn("guarded_two_step_known_points", names)
         self.assertIn("guarded_stagewise_release", names)
@@ -82,6 +83,11 @@ def test_default_experiments_adds_known_point_presets(self):
             for spec in experiments
             if spec.name == "intrinsics_first_guarded_stagewise_release"
         )
+        alternating_spec = next(
+            spec
+            for spec in experiments
+            if spec.name == "intrinsics_first_alternating_stagewise_release"
+        )
         staged_spec = next(
             spec for spec in experiments if spec.name == "guarded_stagewise_release"
         )
@@ -120,6 +126,17 @@ def test_default_experiments_adds_known_point_presets(self):
         self.assertTrue(
             intrinsics_first_spec.ba_kwargs["pose_stage_configs"][1]["optimize_points"]
         )
+        self.assertEqual(alternating_spec.mode, "alternating")
+        self.assertEqual(
+            len(alternating_spec.ba_kwargs["pose_block_configs"]),
+            2,
+        )
+        self.assertFalse(
+            alternating_spec.ba_kwargs["pose_block_configs"][0]["optimize_points"]
+        )
+        self.assertTrue(
+            alternating_spec.ba_kwargs["pose_block_configs"][1]["optimize_points"]
+        )
         self.assertIs(staged_known_spec.ba_kwargs["known_points"], known_points)
         self.assertEqual(staged_known_spec.ba_kwargs["known_point_sigmas"], 0.25)
         self.assertTrue(
@@ -208,6 +225,73 @@ def test_run_experiment_intrinsics_then_guarded_executes_warmstart_first(self):
         self.assertIn("warmstart_rms=3.800000", result.notes)
         self.assertIn("accepted_stage=intrinsics", result.notes)
 
+    def test_run_experiment_alternating_mode_uses_alternating_solver(self):
+        control = ControlPar(4).from_file(
+            Path("tests/testing_folder/control_parameters/control.par")
+        )
+        add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
+        reference_cals = [
+            read_calibration(
+                Path(f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"),
+                add_file,
+            )
+            for cam_num in range(1, 5)
+        ]
+        start_cals = perturb_calibrations(reference_cals, 1.0)
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(reference_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, control.mm),
+                control,
+            )
+
+        spec = next(
+            experiment
+            for experiment in default_experiments(perturbation_scale=1.0)
+            if experiment.name == "intrinsics_first_alternating_stagewise_release"
+        )
+
+        with patch(
+            "openptv_python.demo_bundle_adjustment.alternating_bundle_adjustment",
+            return_value=(
+                reference_cals,
+                points.copy(),
+                {
+                    "warmstart_ok": True,
+                    "accepted_stage": "warmstart",
+                    "final_reprojection_rms": 3.9,
+                    "final_mean_ray_convergence": 0.2,
+                },
+            ),
+        ) as alternating:
+            result = run_experiment(
+                spec,
+                observed_pixels=observed_pixels,
+                point_init=points,
+                control=control,
+                start_cals=start_cals,
+                reference_cals=reference_cals,
+                reference_geometry_points=None,
+                tracking_data=None,
+                geometry_export_threshold=None,
+                correspondence_export_threshold=None,
+                source_case_dir=Path("tests/testing_folder"),
+                output_dir=None,
+            )
+
+        self.assertEqual(alternating.call_count, 1)
+        self.assertIn("warmstart_ok=True", result.notes)
+        self.assertIn("accepted_stage=warmstart", result.notes)
+
     def test_default_experiments_accepts_geometry_guard_configuration(self):
         experiments = default_experiments(
             num_cams=4,

From badefdb2a77bc1dcaaf4cc610ecbaa10a9b5f31a Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 16:26:26 +0200
Subject: [PATCH 13/27] updated after new demo

---
 mypy.ini                                 |   2 +-
 openptv_python/demo_bundle_adjustment.py | 197 +++++++++++---
 openptv_python/orientation.py            | 222 +++++++++++----
 tests/test_bundle_adjustment.py          | 331 ++++++++++++++++++++++-
 tests/test_demo_bundle_adjustment.py     | 135 ++++++++-
 uv.lock                                  | 266 ------------------
 6 files changed, 774 insertions(+), 379 deletions(-)

diff --git a/mypy.ini b/mypy.ini
index 1f68091..cc3fa74 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -1,6 +1,6 @@
 [mypy]
 ignore_missing_imports = True
-exclude = (^build/|^docs/_build/)
+exclude = (^build/|^docs/_build/|^tmp/)
 ; disallow_untyped_defs = True
 ; disallow_incomplete_defs = True
 ; check_untyped_defs = True
diff --git a/openptv_python/demo_bundle_adjustment.py b/openptv_python/demo_bundle_adjustment.py
index 778be24..9f0668f 100644
--- a/openptv_python/demo_bundle_adjustment.py
+++ b/openptv_python/demo_bundle_adjustment.py
@@ -1128,6 +1128,121 @@ def default_experiments(
             },
         },
     ]
+    intrinsics_first_alternating_pose_block_configs = [
+        {
+            "name": "points_only",
+            "optimize_extrinsics": False,
+            "optimize_points": True,
+            "max_nfev": 4,
+            "x_scale": {
+                "points": 0.1,
+            },
+        },
+        {
+            "name": "omega_only",
+            "optimize_extrinsics": True,
+            "optimize_points": False,
+            "freeze_translation": True,
+            "frozen_parameters": ["phi", "kappa"],
+            "prior_sigmas": {
+                "omega": 2e-4,
+            },
+            "parameter_bounds": {
+                "omega": (-0.001, 0.001),
+            },
+            "max_nfev": 3,
+            "x_scale": {
+                "omega": 1e-4,
+            },
+        },
+        {
+            "name": "phi_only",
+            "optimize_extrinsics": True,
+            "optimize_points": False,
+            "freeze_translation": True,
+            "frozen_parameters": ["omega", "kappa"],
+            "prior_sigmas": {
+                "phi": 2e-4,
+            },
+            "parameter_bounds": {
+                "phi": (-0.001, 0.001),
+            },
+            "max_nfev": 3,
+            "x_scale": {
+                "phi": 1e-4,
+            },
+        },
+        {
+            "name": "kappa_only",
+            "optimize_extrinsics": True,
+            "optimize_points": False,
+            "freeze_translation": True,
+            "frozen_parameters": ["omega", "phi"],
+            "prior_sigmas": {
+                "kappa": 2e-4,
+            },
+            "parameter_bounds": {
+                "kappa": (-0.001, 0.001),
+            },
+            "max_nfev": 3,
+            "x_scale": {
+                "kappa": 1e-4,
+            },
+        },
+        {
+            "name": "translation_only",
+            "optimize_extrinsics": True,
+            "optimize_points": False,
+            "freeze_rotation": True,
+            "prior_sigmas": {
+                "x0": 0.02,
+                "y0": 0.02,
+                "z0": 0.02,
+            },
+            "parameter_bounds": {
+                "x0": (-0.05, 0.05),
+                "y0": (-0.05, 0.05),
+                "z0": (-0.05, 0.05),
+            },
+            "max_nfev": 4,
+            "x_scale": {
+                "x0": 0.01,
+                "y0": 0.01,
+                "z0": 0.01,
+            },
+        },
+        {
+            "name": "joint_pose_points",
+            "optimize_extrinsics": True,
+            "optimize_points": True,
+            "prior_sigmas": {
+                "x0": 0.05,
+                "y0": 0.05,
+                "z0": 0.05,
+                "omega": 5e-4,
+                "phi": 5e-4,
+                "kappa": 5e-4,
+            },
+            "parameter_bounds": {
+                "x0": (-0.1, 0.1),
+                "y0": (-0.1, 0.1),
+                "z0": (-0.1, 0.1),
+                "omega": (-0.002, 0.002),
+                "phi": (-0.002, 0.002),
+                "kappa": (-0.002, 0.002),
+            },
+            "max_nfev": 4,
+            "x_scale": {
+                "x0": 0.02,
+                "y0": 0.02,
+                "z0": 0.02,
+                "omega": 2e-4,
+                "phi": 2e-4,
+                "kappa": 2e-4,
+                "points": 0.1,
+            },
+        },
+    ]
     known_point_pose_stage_configs = [
         {
             **stage_config,
@@ -1387,15 +1502,17 @@ def default_experiments(
                     "kappa": 2e-4,
                     "points": 0.1,
                 },
-                "pose_block_configs": intrinsics_first_pose_stage_configs,
+                "pose_block_configs": intrinsics_first_alternating_pose_block_configs,
                 "intrinsic_prior_sigmas": tight_intrinsic_priors,
                 "intrinsic_parameter_bounds": tight_intrinsic_bounds,
                 "intrinsic_max_nfev": 4,
                 "geometry_guard_mode": geometry_guard_mode,
                 "geometry_guard_threshold": geometry_guard_threshold,
+                "first_release_geometry_slack": 0.35,
                 "correspondence_guard_mode": correspondence_guard_mode,
                 "correspondence_guard_threshold": correspondence_guard_threshold,
                 "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
+                "first_release_correspondence_slack": 0.02,
             },
         ),
     ]
@@ -1437,7 +1554,6 @@ def default_experiments(
                             "omega": 5e-4,
                             "phi": 5e-4,
                             "kappa": 5e-4,
-                            "points": 0.1,
                         },
                         "pose_stage_configs": known_point_pose_stage_configs,
                         "intrinsic_prior_sigmas": tight_intrinsic_priors,
@@ -1724,40 +1840,44 @@ def run_experiment(
         notes = f"accepted_stage={summary['accepted_stage']}"
     elif spec.mode == "intrinsics_then_guarded":
         warmstart_orient_par = cast(OrientPar, spec.ba_kwargs["warmstart_orient_par"])
-        warmstart_cals, warmstart_points, warmstart_result = multi_camera_bundle_adjustment(
-            observed_pixels,
-            working_cals,
-            control,
-            warmstart_orient_par,
-            point_init=point_init,
-            fix_first_camera=cast(bool, spec.ba_kwargs.get("fix_first_camera", True)),
-            fixed_camera_indices=cast(
-                List[int] | None,
-                spec.ba_kwargs.get("warmstart_fixed_camera_indices"),
-            ),
-            loss=cast(str, spec.ba_kwargs.get("warmstart_loss", "linear")),
-            method=cast(str, spec.ba_kwargs.get("warmstart_method", "trf")),
-            prior_sigmas=cast(
-                Dict[str, float] | None,
-                spec.ba_kwargs.get("warmstart_prior_sigmas"),
-            ),
-            parameter_bounds=cast(
-                Dict[str, tuple[float, float]] | None,
-                spec.ba_kwargs.get("warmstart_parameter_bounds"),
-            ),
-            max_nfev=cast(int | None, spec.ba_kwargs.get("warmstart_max_nfev")),
-            optimize_extrinsics=cast(
-                bool,
-                spec.ba_kwargs.get("warmstart_optimize_extrinsics", False),
-            ),
-            optimize_points=cast(
-                bool,
-                spec.ba_kwargs.get("warmstart_optimize_points", False),
-            ),
-            x_scale=cast(
-                float | Sequence[float] | Dict[str, float] | None,
-                spec.ba_kwargs.get("warmstart_x_scale"),
-            ),
+        warmstart_cals, warmstart_points, warmstart_result = (
+            multi_camera_bundle_adjustment(
+                observed_pixels,
+                working_cals,
+                control,
+                warmstart_orient_par,
+                point_init=point_init,
+                fix_first_camera=cast(
+                    bool, spec.ba_kwargs.get("fix_first_camera", True)
+                ),
+                fixed_camera_indices=cast(
+                    List[int] | None,
+                    spec.ba_kwargs.get("warmstart_fixed_camera_indices"),
+                ),
+                loss=cast(str, spec.ba_kwargs.get("warmstart_loss", "linear")),
+                method=cast(str, spec.ba_kwargs.get("warmstart_method", "trf")),
+                prior_sigmas=cast(
+                    Dict[str, float] | None,
+                    spec.ba_kwargs.get("warmstart_prior_sigmas"),
+                ),
+                parameter_bounds=cast(
+                    Dict[str, tuple[float, float]] | None,
+                    spec.ba_kwargs.get("warmstart_parameter_bounds"),
+                ),
+                max_nfev=cast(int | None, spec.ba_kwargs.get("warmstart_max_nfev")),
+                optimize_extrinsics=cast(
+                    bool,
+                    spec.ba_kwargs.get("warmstart_optimize_extrinsics", False),
+                ),
+                optimize_points=cast(
+                    bool,
+                    spec.ba_kwargs.get("warmstart_optimize_points", False),
+                ),
+                x_scale=cast(
+                    float | Sequence[float] | Dict[str, float] | None,
+                    spec.ba_kwargs.get("warmstart_x_scale"),
+                ),
+            )
         )
         pose_orient_par = cast(OrientPar, spec.ba_kwargs["pose_orient_par"])
         intrinsic_orient_par = cast(OrientPar, spec.ba_kwargs["intrinsic_orient_par"])
@@ -1897,8 +2017,9 @@ def run_experiment(
         )
         final_rms = cast(float, summary["final_reprojection_rms"])
         final_ray = cast(float, summary["final_mean_ray_convergence"])
-        warmstart_rms = float(
-            cast(Dict[str, object], warmstart_result)["final_reprojection_rms"]
+        warmstart_rms = cast(
+            float,
+            cast(Dict[str, object], warmstart_result)["final_reprojection_rms"],
         )
         success = warmstart_success
         notes = (
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index b273ce6..e996505 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -1355,8 +1355,18 @@ def _expand_parameter_limits(
                 continue
             low_delta, high_delta = limits[name]
             base_value = base_blocks[block_index][param_index]
-            lower.append(base_value + low_delta)
-            upper.append(base_value + high_delta)
+            lower_bound = base_value + low_delta
+            upper_bound = base_value + high_delta
+            if (
+                np.isfinite(lower_bound)
+                and np.isfinite(upper_bound)
+                and lower_bound == upper_bound
+            ):
+                epsilon = 32.0 * np.finfo(np.float64).eps * max(1.0, abs(lower_bound))
+                lower_bound -= epsilon
+                upper_bound += epsilon
+            lower.append(lower_bound)
+            upper.append(upper_bound)
 
     return np.asarray(lower, dtype=np.float64), np.asarray(upper, dtype=np.float64)
 
@@ -1396,16 +1406,16 @@ def _bundle_adjustment_x_scale(
 
         return np.asarray(values, dtype=np.float64)
 
-    values = np.asarray(x_scale, dtype=np.float64)
-    if values.ndim != 1:
+    sequence_values = np.asarray(x_scale, dtype=np.float64)
+    if sequence_values.ndim != 1:
         raise ValueError("x_scale sequence must be one-dimensional")
-    if values.size != total_parameters:
+    if sequence_values.size != total_parameters:
         raise ValueError(
-            f"x_scale sequence must have length {total_parameters}, got {values.size}"
+            f"x_scale sequence must have length {total_parameters}, got {sequence_values.size}"
         )
-    if np.any(values <= 0):
+    if np.any(sequence_values <= 0):
         raise ValueError("x_scale entries must be strictly positive")
-    return values.copy()
+    return sequence_values.copy()
 
 
 def _bundle_adjustment_jacobian_sparsity(
@@ -2100,29 +2110,33 @@ def correspondence_stage_ok(
         pose_correspondence_ok = True
 
         for micro_stage_index, variant in enumerate(pose_stage_variants_list, start=1):
-            candidate_cals, candidate_points, candidate_result = multi_camera_bundle_adjustment(
-                observed_pixels,
-                stage_cals,
-                cpar,
-                pose_orient_par,
-                point_init=stage_points,
-                fixed_camera_indices=fixed_camera_indices,
-                loss=cast(str, variant["loss"]),
-                method=cast(str, variant["method"]),
-                prior_sigmas=cast(Optional[Dict[str, float]], variant["prior_sigmas"]),
-                parameter_bounds=cast(
-                    Optional[Dict[str, Tuple[float, float]]],
-                    variant["parameter_bounds"],
-                ),
-                max_nfev=cast(Optional[int], variant["max_nfev"]),
-                optimize_extrinsics=True,
-                optimize_points=cast(bool, variant["optimize_points"]),
-                known_points=known_points,
-                known_point_sigmas=known_point_sigmas,
-                x_scale=cast(
-                    Optional[float | Sequence[float] | Dict[str, float]],
-                    variant["x_scale"],
-                ),
+            candidate_cals, candidate_points, candidate_result = (
+                multi_camera_bundle_adjustment(
+                    observed_pixels,
+                    stage_cals,
+                    cpar,
+                    pose_orient_par,
+                    point_init=stage_points,
+                    fixed_camera_indices=fixed_camera_indices,
+                    loss=cast(str, variant["loss"]),
+                    method=cast(str, variant["method"]),
+                    prior_sigmas=cast(
+                        Optional[Dict[str, float]], variant["prior_sigmas"]
+                    ),
+                    parameter_bounds=cast(
+                        Optional[Dict[str, Tuple[float, float]]],
+                        variant["parameter_bounds"],
+                    ),
+                    max_nfev=cast(Optional[int], variant["max_nfev"]),
+                    optimize_extrinsics=True,
+                    optimize_points=cast(bool, variant["optimize_points"]),
+                    known_points=known_points,
+                    known_point_sigmas=known_point_sigmas,
+                    x_scale=cast(
+                        Optional[float | Sequence[float] | Dict[str, float]],
+                        variant["x_scale"],
+                    ),
+                )
             )
             candidate_rms = reprojection_rms(
                 observed_pixels, candidate_points, candidate_cals, cpar
@@ -2155,7 +2169,8 @@ def correspondence_stage_ok(
             )
             pose_ok = candidate_rms <= stage_rms and (
                 not reject_on_ray_convergence
-                or candidate_ray_convergence <= stage_ray_convergence + pose_stage_ray_slack
+                or candidate_ray_convergence
+                <= stage_ray_convergence + pose_stage_ray_slack
             )
             pose_ok = pose_ok and pose_geometry_ok and pose_correspondence_ok
             pose_stage_summaries.append(
@@ -2521,18 +2536,24 @@ def alternating_bundle_adjustment(
     geometry_reference_cals: Optional[List[Calibration]] = None,
     geometry_guard_mode: str = "off",
     geometry_guard_threshold: Optional[float] = None,
+    first_release_geometry_slack: float = 0.0,
     correspondence_original_ids: Optional[np.ndarray] = None,
     correspondence_point_frame_indices: Optional[np.ndarray] = None,
     correspondence_frame_target_pixels: Optional[Sequence[Sequence[np.ndarray]]] = None,
     correspondence_guard_mode: str = "off",
     correspondence_guard_threshold: Optional[float] = None,
     correspondence_guard_reference_rate: Optional[float] = None,
+    first_release_correspondence_slack: float = 0.0,
     reject_worse_solution: bool = True,
     reject_on_ray_convergence: bool = True,
 ) -> Tuple[List[Calibration], np.ndarray, Dict[str, object]]:
-    """Run intrinsics-first alternating BA with point/rotation/translation block updates."""
+    """Run intrinsics-first alternating BA with point and pose sub-block updates."""
     if pose_stage_ray_slack < 0:
         raise ValueError("pose_stage_ray_slack must be non-negative")
+    if first_release_geometry_slack < 0:
+        raise ValueError("first_release_geometry_slack must be non-negative")
+    if first_release_correspondence_slack < 0:
+        raise ValueError("first_release_correspondence_slack must be non-negative")
 
     def merge_bounds(
         base: Optional[Dict[str, Tuple[float, float]]],
@@ -2555,18 +2576,43 @@ def default_pose_blocks() -> List[Dict[str, object]]:
                 "x_scale": {"points": 0.1},
             },
             {
-                "name": "rotation_only",
+                "name": "omega_only",
                 "optimize_extrinsics": True,
                 "optimize_points": False,
                 "freeze_translation": True,
                 "loss": pose_loss,
                 "method": pose_method,
-                "max_nfev": 4 if pose_max_nfev is None else min(4, pose_max_nfev),
+                "max_nfev": 3 if pose_max_nfev is None else min(3, pose_max_nfev),
                 "x_scale": {
                     "omega": 2e-4,
+                },
+                "frozen_parameters": ["phi", "kappa"],
+            },
+            {
+                "name": "phi_only",
+                "optimize_extrinsics": True,
+                "optimize_points": False,
+                "freeze_translation": True,
+                "loss": pose_loss,
+                "method": pose_method,
+                "max_nfev": 3 if pose_max_nfev is None else min(3, pose_max_nfev),
+                "x_scale": {
                     "phi": 2e-4,
+                },
+                "frozen_parameters": ["omega", "kappa"],
+            },
+            {
+                "name": "kappa_only",
+                "optimize_extrinsics": True,
+                "optimize_points": False,
+                "freeze_translation": True,
+                "loss": pose_loss,
+                "method": pose_method,
+                "max_nfev": 3 if pose_max_nfev is None else min(3, pose_max_nfev),
+                "x_scale": {
                     "kappa": 2e-4,
                 },
+                "frozen_parameters": ["omega", "phi"],
             },
             {
                 "name": "translation_only",
@@ -2643,6 +2689,8 @@ def max_projection_drift(
     def geometry_stage_ok(
         candidate_metric: Optional[float],
         baseline_metric: Optional[float],
+        *,
+        threshold_override: Optional[float] = None,
     ) -> bool:
         if geometry_guard_mode == "off" or candidate_metric is None:
             return True
@@ -2651,11 +2699,16 @@ def geometry_stage_ok(
                 return True
             return candidate_metric <= baseline_metric + 1e-12
         if geometry_guard_mode == "hard":
-            if geometry_guard_threshold is None or geometry_guard_threshold <= 0:
+            threshold = (
+                geometry_guard_threshold
+                if threshold_override is None
+                else threshold_override
+            )
+            if threshold is None or threshold <= 0:
                 raise ValueError(
                     "geometry_guard_threshold must be positive when geometry_guard_mode='hard'"
                 )
-            return candidate_metric <= geometry_guard_threshold
+            return candidate_metric <= threshold
         raise ValueError("geometry_guard_mode must be one of 'off', 'soft', or 'hard'")
 
     def correspondence_replacement_summary(
@@ -2724,6 +2777,8 @@ def correspondence_replacement_summary(
     def correspondence_stage_ok(
         candidate_rate: Optional[float],
         prior_rate: Optional[float],
+        *,
+        threshold_override: Optional[float] = None,
     ) -> bool:
         if correspondence_guard_mode == "off" or candidate_rate is None:
             return True
@@ -2734,14 +2789,19 @@ def correspondence_stage_ok(
                 return True
             return candidate_rate <= prior_rate + 1e-12
         if correspondence_guard_mode == "hard":
-            if (
-                correspondence_guard_threshold is None
-                or correspondence_guard_threshold <= 0
-            ):
+            if threshold_override is None and correspondence_guard_threshold is None:
+                threshold = None
+            else:
+                threshold = (
+                    correspondence_guard_threshold
+                    if threshold_override is None
+                    else threshold_override
+                )
+            if threshold is None or threshold <= 0:
                 raise ValueError(
                     "correspondence_guard_threshold must be positive when correspondence_guard_mode='hard'"
                 )
-            return candidate_rate <= correspondence_guard_threshold
+            return candidate_rate <= threshold
         raise ValueError(
             "correspondence_guard_mode must be one of 'off', 'soft', or 'hard'"
         )
@@ -2797,8 +2857,12 @@ def correspondence_stage_ok(
         xtol=intrinsic_xtol,
         gtol=intrinsic_gtol,
     )
-    warmstart_rms = reprojection_rms(observed_pixels, warmstart_points, warmstart_cals, cpar)
-    warmstart_ray_convergence = mean_ray_convergence(observed_pixels, warmstart_cals, cpar)
+    warmstart_rms = reprojection_rms(
+        observed_pixels, warmstart_points, warmstart_cals, cpar
+    )
+    warmstart_ray_convergence = mean_ray_convergence(
+        observed_pixels, warmstart_cals, cpar
+    )
     warmstart_geometry = projection_drift_summaries(
         geometry_reference_cals,
         warmstart_cals,
@@ -2853,7 +2917,9 @@ def correspondence_stage_ok(
             if camera_index not in (fixed_camera_indices or [])
         ]
     else:
-        release_order = [int(camera_index) for camera_index in pose_release_camera_order]
+        release_order = [
+            int(camera_index) for camera_index in pose_release_camera_order
+        ]
     if not release_order:
         raise ValueError("pose_release_camera_order must not be empty")
 
@@ -2889,18 +2955,27 @@ def correspondence_stage_ok(
                     block_config.get("parameter_bounds"),
                 ),
             )
+            frozen_parameters = set(
+                cast(Sequence[str], block_config.get("frozen_parameters", []))
+            )
             if cast(bool, block_config.get("freeze_translation", False)):
-                block_bounds = merge_bounds(
-                    block_bounds,
-                    {"x0": (0.0, 0.0), "y0": (0.0, 0.0), "z0": (0.0, 0.0)},
-                )
+                frozen_parameters.update({"x0", "y0", "z0"})
             if cast(bool, block_config.get("freeze_rotation", False)):
+                frozen_parameters.update({"omega", "phi", "kappa"})
+            unknown_frozen = frozen_parameters.difference(
+                {"x0", "y0", "z0", "omega", "phi", "kappa"}
+            )
+            if unknown_frozen:
+                raise ValueError(
+                    "Unsupported frozen_parameters entries: "
+                    + ", ".join(sorted(unknown_frozen))
+                )
+            if frozen_parameters:
                 block_bounds = merge_bounds(
                     block_bounds,
                     {
-                        "omega": (0.0, 0.0),
-                        "phi": (0.0, 0.0),
-                        "kappa": (0.0, 0.0),
+                        parameter_name: (0.0, 0.0)
+                        for parameter_name in sorted(frozen_parameters)
                     },
                 )
 
@@ -2920,7 +2995,9 @@ def correspondence_stage_ok(
                     block_config.get("prior_sigmas", pose_prior_sigmas),
                 ),
                 parameter_bounds=block_bounds,
-                max_nfev=cast(Optional[int], block_config.get("max_nfev", pose_max_nfev)),
+                max_nfev=cast(
+                    Optional[int], block_config.get("max_nfev", pose_max_nfev)
+                ),
                 optimize_extrinsics=optimize_extrinsics,
                 optimize_points=optimize_points,
                 known_points=block_known_points,
@@ -2930,7 +3007,9 @@ def correspondence_stage_ok(
                     block_config.get("x_scale", pose_x_scale),
                 ),
             )
-            block_rms = reprojection_rms(observed_pixels, block_points, block_cals, cpar)
+            block_rms = reprojection_rms(
+                observed_pixels, block_points, block_cals, cpar
+            )
             block_ray_convergence = mean_ray_convergence(
                 observed_pixels,
                 block_cals,
@@ -2951,17 +3030,46 @@ def correspondence_stage_ok(
                 if block_correspondence is None
                 else cast(float, block_correspondence["replacement_rate"])
             )
+            first_release_soft_geometry = (
+                stage_index == 1
+                and optimize_extrinsics
+                and geometry_guard_mode == "hard"
+                and first_release_geometry_slack > 0
+                and geometry_guard_threshold is not None
+            )
+            first_release_soft_correspondence = (
+                stage_index == 1
+                and optimize_extrinsics
+                and correspondence_guard_mode == "hard"
+                and first_release_correspondence_slack > 0
+                and correspondence_guard_threshold is not None
+            )
+            geometry_threshold_override = None
+            if first_release_soft_geometry:
+                geometry_guard_limit = cast(float, geometry_guard_threshold)
+                geometry_threshold_override = (
+                    geometry_guard_limit + first_release_geometry_slack
+                )
+            correspondence_threshold_override = None
+            if first_release_soft_correspondence:
+                correspondence_guard_limit = cast(float, correspondence_guard_threshold)
+                correspondence_threshold_override = (
+                    correspondence_guard_limit + first_release_correspondence_slack
+                )
             block_geometry_ok = geometry_stage_ok(
                 block_geometry_max,
                 current_geometry_max,
+                threshold_override=geometry_threshold_override,
             )
             block_correspondence_ok = correspondence_stage_ok(
                 block_correspondence_rate,
                 current_correspondence_rate,
+                threshold_override=correspondence_threshold_override,
             )
             block_ok = block_rms <= current_rms and (
                 not reject_on_ray_convergence
-                or block_ray_convergence <= current_ray_convergence + pose_stage_ray_slack
+                or block_ray_convergence
+                <= current_ray_convergence + pose_stage_ray_slack
             )
             block_ok = block_ok and block_geometry_ok and block_correspondence_ok
             block_summaries.append(
@@ -2977,9 +3085,11 @@ def correspondence_stage_ok(
                     "geometry": block_geometry,
                     "geometry_max": block_geometry_max,
                     "geometry_ok": block_geometry_ok,
+                    "first_release_soft_geometry": first_release_soft_geometry,
                     "correspondence": block_correspondence,
                     "correspondence_rate": block_correspondence_rate,
                     "correspondence_ok": block_correspondence_ok,
+                    "first_release_soft_correspondence": first_release_soft_correspondence,
                     "accepted": block_ok or not reject_worse_solution,
                     "optimize_extrinsics": optimize_extrinsics,
                     "optimize_points": optimize_points,
@@ -3142,9 +3252,11 @@ def correspondence_stage_ok(
         "intrinsic_correspondence_rate": intrinsic_correspondence_rate,
         "geometry_guard_mode": geometry_guard_mode,
         "geometry_guard_threshold": geometry_guard_threshold,
+        "first_release_geometry_slack": first_release_geometry_slack,
         "correspondence_guard_mode": correspondence_guard_mode,
         "correspondence_guard_threshold": correspondence_guard_threshold,
         "correspondence_guard_reference_rate": correspondence_guard_reference_rate,
+        "first_release_correspondence_slack": first_release_correspondence_slack,
         "accepted_stage": accepted_stage,
         "final_reprojection_rms": final_rms,
         "final_mean_ray_convergence": final_ray_convergence,
diff --git a/tests/test_bundle_adjustment.py b/tests/test_bundle_adjustment.py
index 21cf302..421e03b 100644
--- a/tests/test_bundle_adjustment.py
+++ b/tests/test_bundle_adjustment.py
@@ -203,6 +203,51 @@ def fake_least_squares(_fun, x0, **kwargs):
         self.assertTrue(result.success)
         np.testing.assert_allclose(refined_points, points)
 
+    def test_multi_camera_bundle_adjustment_accepts_zero_width_parameter_bounds(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        start_cals = self.lightly_perturb_calibrations(self.true_cals)
+        refined_cals, refined_points, result = multi_camera_bundle_adjustment(
+            observed_pixels,
+            start_cals,
+            self.control,
+            OrientPar(),
+            point_init=points.copy(),
+            fixed_camera_indices=[0, 1, 2],
+            optimize_points=False,
+            loss="linear",
+            method="trf",
+            max_nfev=5,
+            parameter_bounds={
+                "phi": (0.0, 0.0),
+            },
+            prior_sigmas={
+                "x0": 1.0,
+                "y0": 1.0,
+                "z0": 1.0,
+                "omega": 0.01,
+                "phi": 0.01,
+                "kappa": 0.01,
+            },
+        )
+
+        self.assertTrue(result.success, msg=result.message)
+        self.assertEqual(refined_points.shape, points.shape)
+        self.assertEqual(len(refined_cals), 4)
+
     def test_cavity_reprojection_improves(self):
         cavity_dir = Path("tests/testing_fodder/test_cavity")
         control = ControlPar(4).from_file(cavity_dir / "parameters/ptv.par")
@@ -928,12 +973,36 @@ def test_alternating_bundle_adjustment_supports_block_schedule(self):
             )
 
         staged_returns = [
-            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 9.5}),
-            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 9.0}),
-            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 8.5}),
-            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 8.0}),
-            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 7.5}),
-            (self.true_cals, points.copy(), {"success": True, "final_reprojection_rms": 7.0}),
+            (
+                self.true_cals,
+                points.copy(),
+                {"success": True, "final_reprojection_rms": 9.5},
+            ),
+            (
+                self.true_cals,
+                points.copy(),
+                {"success": True, "final_reprojection_rms": 9.0},
+            ),
+            (
+                self.true_cals,
+                points.copy(),
+                {"success": True, "final_reprojection_rms": 8.5},
+            ),
+            (
+                self.true_cals,
+                points.copy(),
+                {"success": True, "final_reprojection_rms": 8.0},
+            ),
+            (
+                self.true_cals,
+                points.copy(),
+                {"success": True, "final_reprojection_rms": 7.5},
+            ),
+            (
+                self.true_cals,
+                points.copy(),
+                {"success": True, "final_reprojection_rms": 7.0},
+            ),
         ]
 
         with (
@@ -985,9 +1054,13 @@ def test_alternating_bundle_adjustment_supports_block_schedule(self):
             fixed_sequences,
             [[0, 1, 2, 3], [1, 2, 3], [1, 2, 3], [2, 3], [2, 3], [0, 1, 2, 3]],
         )
-        self.assertFalse(mocked_adjustment.call_args_list[0].kwargs["optimize_extrinsics"])
+        self.assertFalse(
+            mocked_adjustment.call_args_list[0].kwargs["optimize_extrinsics"]
+        )
         self.assertFalse(mocked_adjustment.call_args_list[0].kwargs["optimize_points"])
-        self.assertFalse(mocked_adjustment.call_args_list[1].kwargs["optimize_extrinsics"])
+        self.assertFalse(
+            mocked_adjustment.call_args_list[1].kwargs["optimize_extrinsics"]
+        )
         self.assertTrue(mocked_adjustment.call_args_list[1].kwargs["optimize_points"])
         self.assertEqual(
             mocked_adjustment.call_args_list[2].kwargs["parameter_bounds"]["x0"],
@@ -1000,6 +1073,248 @@ def test_alternating_bundle_adjustment_supports_block_schedule(self):
             ["points_only", "rotation_only", "points_only", "rotation_only"],
         )
 
+    def test_alternating_bundle_adjustment_freezes_requested_rotation_axes(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        staged_returns = [
+            (
+                self.true_cals,
+                points.copy(),
+                {"success": True, "final_reprojection_rms": 9.5},
+            ),
+            (
+                self.true_cals,
+                points.copy(),
+                {"success": True, "final_reprojection_rms": 9.0},
+            ),
+            (
+                self.true_cals,
+                points.copy(),
+                {"success": True, "final_reprojection_rms": 8.5},
+            ),
+        ]
+
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=staged_returns,
+            ) as mocked_adjustment,
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 9.5, 9.0, 8.5],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[6.0, 5.5, 5.0, 4.5],
+            ),
+        ):
+            _, _, summary = alternating_bundle_adjustment(
+                observed_pixels,
+                self.lightly_perturb_calibrations(self.true_cals),
+                self.control,
+                OrientPar(),
+                OrientPar(),
+                point_init=points,
+                pose_release_camera_order=[0],
+                pose_block_configs=[
+                    {
+                        "name": "omega_only",
+                        "optimize_extrinsics": True,
+                        "optimize_points": False,
+                        "freeze_translation": True,
+                        "frozen_parameters": ["phi", "kappa"],
+                        "max_nfev": 3,
+                    },
+                ],
+                intrinsic_max_nfev=4,
+            )
+
+        self.assertEqual(summary["accepted_pose_block_count"], 1)
+        self.assertEqual(
+            mocked_adjustment.call_args_list[1].kwargs["parameter_bounds"]["x0"],
+            (0.0, 0.0),
+        )
+        self.assertEqual(
+            mocked_adjustment.call_args_list[1].kwargs["parameter_bounds"]["phi"],
+            (0.0, 0.0),
+        )
+        self.assertEqual(
+            mocked_adjustment.call_args_list[1].kwargs["parameter_bounds"]["kappa"],
+            (0.0, 0.0),
+        )
+
+    def test_alternating_bundle_adjustment_softens_first_release_geometry_guard(self):
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(self.true_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, self.control.mm), self.control
+            )
+
+        shifted_cals = [self.clone_calibration(cal) for cal in self.true_cals]
+        shifted_cals[0].set_angles(
+            shifted_cals[0].get_angles() + np.array([0.0, 0.0015, 0.0])
+        )
+        geometry_reference_points = np.array(
+            [
+                [-15.0, -10.0, -2.0],
+                [12.0, -8.0, 1.0],
+                [-9.0, 14.0, 3.0],
+                [8.0, 6.0, -1.0],
+            ],
+            dtype=float,
+        )
+
+        def run_with_threshold(
+            threshold: float,
+            slack: float,
+        ) -> dict[str, object]:
+            with (
+                patch(
+                    "openptv_python.orientation.multi_camera_bundle_adjustment",
+                    side_effect=[
+                        (
+                            self.true_cals,
+                            points.copy(),
+                            {"success": True, "stage": "warmstart"},
+                        ),
+                        (
+                            shifted_cals,
+                            points.copy(),
+                            {"success": True, "stage": "omega"},
+                        ),
+                        (
+                            self.true_cals,
+                            points.copy(),
+                            {"success": True, "stage": "intrinsics"},
+                        ),
+                    ],
+                ),
+                patch(
+                    "openptv_python.orientation.reprojection_rms",
+                    side_effect=[10.0, 9.8, 9.6, 9.5],
+                ),
+                patch(
+                    "openptv_python.orientation.mean_ray_convergence",
+                    side_effect=[6.0, 5.9, 5.8, 5.7],
+                ),
+            ):
+                _, _, summary = alternating_bundle_adjustment(
+                    observed_pixels,
+                    [self.clone_calibration(cal) for cal in self.true_cals],
+                    self.control,
+                    OrientPar(),
+                    OrientPar(),
+                    point_init=points,
+                    pose_release_camera_order=[0],
+                    pose_block_configs=[
+                        {
+                            "name": "omega_only",
+                            "optimize_extrinsics": True,
+                            "optimize_points": False,
+                            "freeze_translation": True,
+                            "frozen_parameters": ["phi", "kappa"],
+                            "max_nfev": 3,
+                        },
+                    ],
+                    geometry_reference_points=geometry_reference_points,
+                    geometry_reference_cals=[
+                        self.clone_calibration(cal) for cal in self.true_cals
+                    ],
+                    geometry_guard_mode="hard",
+                    geometry_guard_threshold=threshold,
+                    first_release_geometry_slack=slack,
+                    intrinsic_max_nfev=4,
+                )
+            return summary
+
+        measured = run_with_threshold(1e6, 0.0)
+        geometry_max = float(measured["pose_block_summaries"][0]["geometry_max"])
+
+        rejected = run_with_threshold(geometry_max - 1e-6, 0.0)
+        rejected_block = rejected["pose_block_summaries"][0]
+        self.assertFalse(rejected_block["accepted"])
+
+        with (
+            patch(
+                "openptv_python.orientation.multi_camera_bundle_adjustment",
+                side_effect=[
+                    (
+                        self.true_cals,
+                        points.copy(),
+                        {"success": True, "stage": "warmstart"},
+                    ),
+                    (shifted_cals, points.copy(), {"success": True, "stage": "omega"}),
+                    (
+                        self.true_cals,
+                        points.copy(),
+                        {"success": True, "stage": "intrinsics"},
+                    ),
+                ],
+            ),
+            patch(
+                "openptv_python.orientation.reprojection_rms",
+                side_effect=[10.0, 9.8, 9.6, 9.5],
+            ),
+            patch(
+                "openptv_python.orientation.mean_ray_convergence",
+                side_effect=[6.0, 5.9, 5.8, 5.7],
+            ),
+        ):
+            _, _, softened = alternating_bundle_adjustment(
+                observed_pixels,
+                [self.clone_calibration(cal) for cal in self.true_cals],
+                self.control,
+                OrientPar(),
+                OrientPar(),
+                point_init=points,
+                pose_release_camera_order=[0],
+                pose_block_configs=[
+                    {
+                        "name": "omega_only",
+                        "optimize_extrinsics": True,
+                        "optimize_points": False,
+                        "freeze_translation": True,
+                        "frozen_parameters": ["phi", "kappa"],
+                        "max_nfev": 3,
+                    },
+                ],
+                geometry_reference_points=geometry_reference_points,
+                geometry_reference_cals=[
+                    self.clone_calibration(cal) for cal in self.true_cals
+                ],
+                geometry_guard_mode="hard",
+                geometry_guard_threshold=geometry_max - 1e-6,
+                first_release_geometry_slack=2e-6,
+                intrinsic_max_nfev=4,
+            )
+
+        softened_block = softened["pose_block_summaries"][0]
+        self.assertTrue(softened_block["accepted"])
+        self.assertTrue(softened_block["first_release_soft_geometry"])
+        self.assertEqual(softened["accepted_pose_block_count"], 1)
+
     def test_guarded_two_step_bundle_adjustment_stagewise_ray_slack_allows_near_miss(
         self,
     ):
diff --git a/tests/test_demo_bundle_adjustment.py b/tests/test_demo_bundle_adjustment.py
index 8d2a683..26c1b5d 100644
--- a/tests/test_demo_bundle_adjustment.py
+++ b/tests/test_demo_bundle_adjustment.py
@@ -6,7 +6,6 @@
 
 from openptv_python.calibration import read_calibration
 from openptv_python.demo_bundle_adjustment import (
-    ExperimentSpec,
     ExperimentResult,
     all_fixed_camera_pairs,
     build_experiment_start_calibrations,
@@ -106,12 +105,27 @@ def test_default_experiments_adds_known_point_presets(self):
         )
         self.assertEqual(staged_spec.ba_kwargs["pose_stage_ray_slack"], 0.0)
         self.assertEqual(len(staged_spec.ba_kwargs["pose_stage_configs"]), 3)
-        self.assertFalse(staged_spec.ba_kwargs["pose_stage_configs"][0]["optimize_points"])
-        self.assertTrue(staged_spec.ba_kwargs["pose_stage_configs"][1]["optimize_points"])
-        self.assertTrue(staged_spec.ba_kwargs["pose_stage_configs"][2]["optimize_points"])
+        self.assertFalse(
+            staged_spec.ba_kwargs["pose_stage_configs"][0]["optimize_points"]
+        )
+        self.assertTrue(
+            staged_spec.ba_kwargs["pose_stage_configs"][1]["optimize_points"]
+        )
+        self.assertTrue(
+            staged_spec.ba_kwargs["pose_stage_configs"][2]["optimize_points"]
+        )
         self.assertEqual(intrinsics_first_spec.mode, "intrinsics_then_guarded")
-        self.assertFalse(intrinsics_first_spec.ba_kwargs["warmstart_optimize_extrinsics"])
+        self.assertFalse(
+            intrinsics_first_spec.ba_kwargs["warmstart_optimize_extrinsics"]
+        )
         self.assertFalse(intrinsics_first_spec.ba_kwargs["warmstart_optimize_points"])
+        self.assertNotIn(
+            "first_release_geometry_slack", intrinsics_first_spec.ba_kwargs
+        )
+        self.assertNotIn(
+            "first_release_correspondence_slack",
+            intrinsics_first_spec.ba_kwargs,
+        )
         self.assertEqual(
             intrinsics_first_spec.ba_kwargs["pose_release_camera_order"],
             [0, 1, 2, 3],
@@ -129,13 +143,31 @@ def test_default_experiments_adds_known_point_presets(self):
         self.assertEqual(alternating_spec.mode, "alternating")
         self.assertEqual(
             len(alternating_spec.ba_kwargs["pose_block_configs"]),
-            2,
+            6,
         )
-        self.assertFalse(
+        self.assertTrue(
             alternating_spec.ba_kwargs["pose_block_configs"][0]["optimize_points"]
         )
-        self.assertTrue(
-            alternating_spec.ba_kwargs["pose_block_configs"][1]["optimize_points"]
+        self.assertEqual(
+            [
+                block["name"]
+                for block in alternating_spec.ba_kwargs["pose_block_configs"]
+            ],
+            [
+                "points_only",
+                "omega_only",
+                "phi_only",
+                "kappa_only",
+                "translation_only",
+                "joint_pose_points",
+            ],
+        )
+        self.assertEqual(
+            alternating_spec.ba_kwargs["first_release_geometry_slack"], 0.35
+        )
+        self.assertEqual(
+            alternating_spec.ba_kwargs["first_release_correspondence_slack"],
+            0.02,
         )
         self.assertIs(staged_known_spec.ba_kwargs["known_points"], known_points)
         self.assertEqual(staged_known_spec.ba_kwargs["known_point_sigmas"], 0.25)
@@ -195,11 +227,23 @@ def test_run_experiment_intrinsics_then_guarded_executes_warmstart_first(self):
         with (
             patch(
                 "openptv_python.demo_bundle_adjustment.multi_camera_bundle_adjustment",
-                return_value=(reference_cals, points.copy(), {"success": True, "final_reprojection_rms": 3.8, "message": "warm"}),
+                return_value=(
+                    reference_cals,
+                    points.copy(),
+                    {"success": True, "final_reprojection_rms": 3.8, "message": "warm"},
+                ),
             ) as warmstart,
             patch(
                 "openptv_python.demo_bundle_adjustment.guarded_two_step_bundle_adjustment",
-                return_value=(reference_cals, points.copy(), {"accepted_stage": "intrinsics", "final_reprojection_rms": 3.7, "final_mean_ray_convergence": 0.2}),
+                return_value=(
+                    reference_cals,
+                    points.copy(),
+                    {
+                        "accepted_stage": "intrinsics",
+                        "final_reprojection_rms": 3.7,
+                        "final_mean_ray_convergence": 0.2,
+                    },
+                ),
             ) as guarded,
         ):
             result = run_experiment(
@@ -225,6 +269,75 @@ def test_run_experiment_intrinsics_then_guarded_executes_warmstart_first(self):
         self.assertIn("warmstart_rms=3.800000", result.notes)
         self.assertIn("accepted_stage=intrinsics", result.notes)
 
+    def test_run_experiment_guarded_mode_does_not_forward_alternating_slack(self):
+        control = ControlPar(4).from_file(
+            Path("tests/testing_folder/control_parameters/control.par")
+        )
+        add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
+        reference_cals = [
+            read_calibration(
+                Path(f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"),
+                add_file,
+            )
+            for cam_num in range(1, 5)
+        ]
+        start_cals = perturb_calibrations(reference_cals, 1.0)
+        points = np.array(
+            [
+                [-10.0, -10.0, 0.0],
+                [10.0, -10.0, 1.0],
+                [-10.0, 10.0, -1.0],
+                [10.0, 10.0, 0.5],
+            ],
+            dtype=float,
+        )
+        observed_pixels = np.empty((len(points), 4, 2), dtype=float)
+        for cam_num, cal in enumerate(reference_cals):
+            observed_pixels[:, cam_num, :] = arr_metric_to_pixel(
+                image_coordinates(points, cal, control.mm),
+                control,
+            )
+
+        spec = next(
+            experiment
+            for experiment in default_experiments(perturbation_scale=1.0)
+            if experiment.name == "guarded_stagewise_release"
+        )
+
+        with patch(
+            "openptv_python.demo_bundle_adjustment.guarded_two_step_bundle_adjustment",
+            return_value=(
+                reference_cals,
+                points.copy(),
+                {
+                    "accepted_stage": "intrinsics",
+                    "final_reprojection_rms": 3.7,
+                    "final_mean_ray_convergence": 0.2,
+                },
+            ),
+        ) as guarded:
+            run_experiment(
+                spec,
+                observed_pixels=observed_pixels,
+                point_init=points,
+                control=control,
+                start_cals=start_cals,
+                reference_cals=reference_cals,
+                reference_geometry_points=None,
+                tracking_data=None,
+                geometry_export_threshold=None,
+                correspondence_export_threshold=None,
+                source_case_dir=Path("tests/testing_folder"),
+                output_dir=None,
+            )
+
+        self.assertEqual(guarded.call_count, 1)
+        self.assertNotIn("first_release_geometry_slack", guarded.call_args.kwargs)
+        self.assertNotIn(
+            "first_release_correspondence_slack",
+            guarded.call_args.kwargs,
+        )
+
     def test_run_experiment_alternating_mode_uses_alternating_solver(self):
         control = ControlPar(4).from_file(
             Path("tests/testing_folder/control_parameters/control.par")
diff --git a/uv.lock b/uv.lock
index fd02693..385e159 100644
--- a/uv.lock
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From 08419d76ba4d33cc6a90f9a3bf6cb4813151c7db Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 19:25:33 +0200
Subject: [PATCH 14/27] created wrapper to use openptv-python or optv
 interchangeably for segmentation and image processing

---
 openptv_python/_native_compat.py        |  69 ++++
 openptv_python/_native_convert.py       | 135 +++++++
 openptv_python/image_processing.py      | 286 +++++++++------
 openptv_python/segmentation.py          |  29 ++
 plans.md                                |   2 +
 pyproject.toml                          |   2 +
 tests/test_image_processing.py          |  71 ++--
 tests/test_native_stress_performance.py | 288 +++++++++++++++
 tests/test_segmentation.py              |  65 ++++
 uv.lock                                 | 444 +++++++++++++++---------
 10 files changed, 1090 insertions(+), 301 deletions(-)
 create mode 100644 openptv_python/_native_compat.py
 create mode 100644 openptv_python/_native_convert.py
 create mode 100644 plans.md
 create mode 100644 tests/test_native_stress_performance.py

diff --git a/openptv_python/_native_compat.py b/openptv_python/_native_compat.py
new file mode 100644
index 0000000..fbb01c6
--- /dev/null
+++ b/openptv_python/_native_compat.py
@@ -0,0 +1,69 @@
+"""Optional compatibility layer for reusing optv py_bind as a native provider."""
+
+from __future__ import annotations
+
+from importlib import import_module
+from types import ModuleType
+from typing import Any
+
+
+def _optional_import(module_name: str) -> ModuleType | None:
+    try:
+        return import_module(module_name)
+    except Exception:
+        return None
+
+
+optv_calibration = _optional_import("optv.calibration")
+optv_image_processing = _optional_import("optv.image_processing")
+optv_parameters = _optional_import("optv.parameters")
+optv_segmentation = _optional_import("optv.segmentation")
+optv_tracking_framebuf = _optional_import("optv.tracking_framebuf")
+
+HAS_OPTV = any(
+    module is not None
+    for module in (
+        optv_calibration,
+        optv_image_processing,
+        optv_parameters,
+        optv_segmentation,
+        optv_tracking_framebuf,
+    )
+)
+
+HAS_NATIVE_PREPROCESS = (
+    optv_image_processing is not None
+    and hasattr(optv_image_processing, "preprocess_image")
+    and optv_parameters is not None
+    and hasattr(optv_parameters, "ControlParams")
+)
+
+HAS_NATIVE_SEGMENTATION = (
+    optv_segmentation is not None
+    and hasattr(optv_segmentation, "target_recognition")
+    and optv_parameters is not None
+    and hasattr(optv_parameters, "TargetParams")
+    and hasattr(optv_parameters, "ControlParams")
+    and optv_tracking_framebuf is not None
+    and hasattr(optv_tracking_framebuf, "Target")
+)
+
+HAS_NATIVE_CALIBRATION = optv_calibration is not None and hasattr(
+    optv_calibration, "Calibration"
+)
+
+HAS_NATIVE_TARGETS = optv_tracking_framebuf is not None and hasattr(
+    optv_tracking_framebuf, "Target"
+)
+
+
+def native_preprocess_image(*args: Any, **kwargs: Any) -> Any:
+    if not HAS_NATIVE_PREPROCESS or optv_image_processing is None:
+        raise RuntimeError("optv native preprocess_image is not available")
+    return optv_image_processing.preprocess_image(*args, **kwargs)
+
+
+def native_target_recognition(*args: Any, **kwargs: Any) -> Any:
+    if not HAS_NATIVE_SEGMENTATION or optv_segmentation is None:
+        raise RuntimeError("optv native target_recognition is not available")
+    return optv_segmentation.target_recognition(*args, **kwargs)
\ No newline at end of file
diff --git a/openptv_python/_native_convert.py b/openptv_python/_native_convert.py
new file mode 100644
index 0000000..001e7ef
--- /dev/null
+++ b/openptv_python/_native_convert.py
@@ -0,0 +1,135 @@
+"""Conversion helpers between openptv_python objects and optv py_bind objects."""
+
+from __future__ import annotations
+
+from typing import Iterable, List
+from types import ModuleType
+
+import numpy as np
+
+from ._native_compat import (
+    HAS_NATIVE_CALIBRATION,
+    HAS_NATIVE_TARGETS,
+    HAS_OPTV,
+    optv_calibration,
+    optv_parameters,
+    optv_tracking_framebuf,
+)
+from .calibration import Calibration
+from .parameters import ControlPar, TargetPar
+from .tracking_frame_buf import Target
+
+
+def _require_optv_parameters() -> None:
+    if not HAS_OPTV or optv_parameters is None:
+        raise RuntimeError("optv py_bind parameters are not available")
+
+
+def _optv_parameters_module() -> ModuleType:
+    _require_optv_parameters()
+    assert optv_parameters is not None
+    return optv_parameters
+
+
+def to_native_calibration(cal: Calibration):
+    if not HAS_NATIVE_CALIBRATION or optv_calibration is None:
+        raise RuntimeError("optv Calibration is not available")
+
+    native = optv_calibration.Calibration()
+    native.set_pos(np.asarray(cal.get_pos(), dtype=np.float64))
+    native.set_angles(np.asarray(cal.get_angles(), dtype=np.float64))
+    native.set_primary_point(np.asarray(cal.get_primary_point(), dtype=np.float64))
+    native.set_radial_distortion(
+        np.asarray(cal.get_radial_distortion(), dtype=np.float64)
+    )
+    native.set_decentering(np.asarray(cal.get_decentering(), dtype=np.float64))
+    native.set_affine_trans(np.asarray(cal.get_affine(), dtype=np.float64))
+    native.set_glass_vec(np.asarray(cal.get_glass_vec(), dtype=np.float64))
+    return native
+
+
+def to_native_control_par(cpar: ControlPar):
+    parameters_module = _optv_parameters_module()
+
+    flags = [
+        flag_name
+        for enabled, flag_name in (
+            (bool(cpar.hp_flag), "hp"),
+            (bool(cpar.all_cam_flag), "allcam"),
+            (bool(cpar.tiff_flag), "headers"),
+        )
+        if enabled
+    ]
+
+    native = parameters_module.ControlParams(
+        cpar.num_cams,
+        flags=flags,
+        image_size=(cpar.imx, cpar.imy),
+        pixel_size=(cpar.pix_x, cpar.pix_y),
+        cam_side_n=cpar.mm.n1,
+        wall_ns=list(cpar.mm.n2),
+        wall_thicks=list(cpar.mm.d),
+        object_side_n=cpar.mm.n3,
+    )
+    native.set_chfield(cpar.chfield)
+
+    for cam_index, img_base_name in enumerate(cpar.img_base_name):
+        native.set_img_base_name(cam_index, img_base_name)
+
+    for cam_index, cal_img_base_name in enumerate(cpar.cal_img_base_name):
+        native.set_cal_img_base_name(cam_index, cal_img_base_name)
+
+    return native
+
+
+def to_native_target_par(tpar: TargetPar):
+    parameters_module = _optv_parameters_module()
+
+    thresholds = list(tpar.gvthresh)
+    if len(thresholds) < 4:
+        thresholds.extend([0] * (4 - len(thresholds)))
+
+    return parameters_module.TargetParams(
+        discont=tpar.discont,
+        gvthresh=thresholds[:4],
+        pixel_count_bounds=(tpar.nnmin, tpar.nnmax),
+        xsize_bounds=(tpar.nxmin, tpar.nxmax),
+        ysize_bounds=(tpar.nymin, tpar.nymax),
+        min_sum_grey=tpar.sumg_min,
+        cross_size=tpar.cr_sz,
+    )
+
+
+def to_native_target(target: Target):
+    if not HAS_NATIVE_TARGETS or optv_tracking_framebuf is None:
+        raise RuntimeError("optv Target is not available")
+
+    return optv_tracking_framebuf.Target(
+        pnr=target.pnr,
+        x=target.x,
+        y=target.y,
+        n=target.n,
+        nx=target.nx,
+        ny=target.ny,
+        sumg=target.sumg,
+        tnr=target.tnr,
+    )
+
+
+def from_native_target(native_target) -> Target:
+    x, y = native_target.pos()
+    n, nx, ny = native_target.count_pixels()
+    return Target(
+        pnr=int(native_target.pnr()),
+        x=float(x),
+        y=float(y),
+        n=int(n),
+        nx=int(nx),
+        ny=int(ny),
+        sumg=int(native_target.sum_grey_value()),
+        tnr=int(native_target.tnr()),
+    )
+
+
+def from_native_target_array(native_targets: Iterable[object]) -> List[Target]:
+    return [from_native_target(target) for target in native_targets]
\ No newline at end of file
diff --git a/openptv_python/image_processing.py b/openptv_python/image_processing.py
index bd47fdb..082e785 100644
--- a/openptv_python/image_processing.py
+++ b/openptv_python/image_processing.py
@@ -3,10 +3,10 @@
 import copy
 
 import numpy as np
-from numba import njit, prange
-from scipy import ndimage
-from scipy.ndimage import uniform_filter
+from numba import njit
 
+from ._native_compat import HAS_NATIVE_PREPROCESS, native_preprocess_image
+from ._native_convert import to_native_control_par
 from .parameters import ControlPar
 
 filter_t = np.zeros((3, 3), dtype=float)
@@ -15,35 +15,169 @@
 @njit
 def filter_3(img, kernel=None) -> np.ndarray:
     """Apply a 3x3 filter to an image."""
-    if kernel is None:  # default is a low pass
-        kernel = np.ones((3, 3)) / 9
-    filtered_img = ndimage.convolve(img, kernel)
-    return filtered_img
+    if img.dtype != np.uint8:
+        raise TypeError("Image must be of type uint8")
+
+    if kernel is None:
+        kernel = np.ones((3, 3), dtype=np.float64)
+
+    kernel_sum = float(np.sum(kernel))
+    if kernel_sum == 0:
+        raise ValueError("Filter kernel sum must not be zero")
+
+    imx = img.shape[1]
+    imy = img.shape[0]
+    image_size = imx * imy
+    flat_in = img.reshape(-1)
+    flat_out = flat_in.copy()
+
+    for index in range(imx + 1, image_size - imx - 1):
+        buf = (
+            kernel[0, 0] * flat_in[index - imx - 1]
+            + kernel[0, 1] * flat_in[index - imx]
+            + kernel[0, 2] * flat_in[index - imx + 1]
+            + kernel[1, 0] * flat_in[index - 1]
+            + kernel[1, 1] * flat_in[index]
+            + kernel[1, 2] * flat_in[index + 1]
+            + kernel[2, 0] * flat_in[index + imx - 1]
+            + kernel[2, 1] * flat_in[index + imx]
+            + kernel[2, 2] * flat_in[index + imx + 1]
+        )
+        buf = int(buf / kernel_sum)
+        if buf > 255:
+            buf = 255
+        if buf < 8:
+            buf = 8
+        flat_out[index] = np.uint8(buf)
+
+    return flat_out.reshape(img.shape)
 
 
 @njit
 def lowpass_3(img: np.ndarray) -> np.ndarray:
-    """Lowpass filter of 3x3."""
-    # Define the 3x3 lowpass filter kernel
-    kernel = np.ones((3, 3)) / 9
+    """Lowpass filter matching the native 3x3 implementation."""
+    if img.dtype != np.uint8:
+        raise TypeError("Image must be of type uint8")
 
-    # Apply the filter to the image using scipy.ndimage.convolve()
-    img_lp = ndimage.convolve(img, kernel, mode="constant", cval=0.0)
+    imx = img.shape[1]
+    imy = img.shape[0]
+    image_size = imx * imy
+    flat_in = img.reshape(-1)
+    flat_out = flat_in.copy()
+
+    for index in range(imx + 1, image_size - imx - 1):
+        buf = (
+            int(flat_in[index - imx - 1])
+            + int(flat_in[index - imx])
+            + int(flat_in[index - imx + 1])
+            + int(flat_in[index - 1])
+            + int(flat_in[index])
+            + int(flat_in[index + 1])
+            + int(flat_in[index + imx - 1])
+            + int(flat_in[index + imx])
+            + int(flat_in[index + imx + 1])
+        )
+        flat_out[index] = np.uint8(buf // 9)
 
-    return img_lp
+    return flat_out.reshape(img.shape)
 
 
-@njit
-def fast_box_blur(filt_span: int, src: np.ndarray, cpar: ControlPar) -> np.ndarray:
-    """Fast box blur."""
+def fast_box_blur(
+    filt_span: int,
+    src: np.ndarray,
+    cpar: ControlPar | None = None,
+) -> np.ndarray:
+    """Fast box blur matching liboptv border handling."""
+    if src.dtype != np.uint8:
+        raise TypeError("Image must be of type uint8")
+
+    if src.ndim != 2:
+        raise TypeError("Input array must be two-dimensional")
+
+    if cpar is not None:
+        imx = int(cpar.imx)
+        imy = int(cpar.imy)
+        if src.shape != (imy, imx):
+            raise ValueError("Image shape does not match control parameters")
+    else:
+        imy, imx = src.shape
+
+    image_size = imx * imy
     n = 2 * filt_span + 1
-    row_accum = uniform_filter(
-        src.reshape((cpar.imy, cpar.imx)),
-        size=n,
-        mode="constant",
-        cval=0,
-    ).reshape(-1)
-    return row_accum
+    nq = n * n
+    src_flat = src.reshape(-1)
+    dest_flat = np.zeros(image_size, dtype=np.uint8)
+    row_accum = np.zeros(image_size, dtype=np.int64)
+    col_accum = np.zeros(imx, dtype=np.int64)
+
+    for row in range(imy):
+        row_start = row * imx
+        accum = int(src_flat[row_start])
+        row_accum[row_start] = accum * n
+
+        for col in range(1, min(filt_span + 1, imx)):
+            right = row_start + 2 * col
+            left = right - 1
+            if right >= row_start + imx:
+                break
+            accum += int(src_flat[left]) + int(src_flat[right])
+            row_accum[row_start + col] = accum * n // (2 * col + 1)
+
+        for col in range(filt_span + 1, max(imx - filt_span, filt_span + 1)):
+            accum += int(src_flat[row_start + col + filt_span])
+            accum -= int(src_flat[row_start + col - filt_span - 1])
+            row_accum[row_start + col] = accum
+
+        m = n - 2
+        col = imx - filt_span
+        left = row_start + imx - n
+        right = left + 1
+        while col < row_start + imx - row_start and m > 0 and right < row_start + imx:
+            accum -= int(src_flat[left]) + int(src_flat[right])
+            row_accum[row_start + col] = accum * n // m
+            left += 2
+            right += 2
+            col += 1
+            m -= 2
+
+    for col in range(imx):
+        col_accum[col] = row_accum[col]
+        dest_flat[col] = np.uint8(col_accum[col] // n)
+
+    max_top = min(filt_span, (imy - 1) // 2)
+    for row in range(1, max_top + 1):
+        base1 = (2 * row - 1) * imx
+        base2 = base1 + imx
+        out_base = row * imx
+        for col in range(imx):
+            col_accum[col] += row_accum[base1 + col] + row_accum[base2 + col]
+            dest_flat[out_base + col] = np.uint8(
+                n * col_accum[col] // nq // (2 * row + 1)
+            )
+
+    for row in range(filt_span + 1, max(imy - filt_span, filt_span + 1)):
+        remove_base = (row - filt_span - 1) * imx
+        add_base = (row + filt_span) * imx
+        out_base = row * imx
+        for col in range(imx):
+            col_accum[col] += row_accum[add_base + col] - row_accum[remove_base + col]
+            dest_flat[out_base + col] = np.uint8(col_accum[col] // nq)
+
+    for remaining in range(min(filt_span, imy - 1), 0, -1):
+        remove_base = (imy - 2 * remaining - 1) * imx
+        remove_base2 = remove_base + imx
+        out_base = (imy - remaining) * imx
+        if remove_base < 0:
+            continue
+        for col in range(imx):
+            col_accum[col] -= (
+                row_accum[remove_base + col] + row_accum[remove_base2 + col]
+            )
+            dest_flat[out_base + col] = np.uint8(
+                n * col_accum[col] // nq // (2 * remaining + 1)
+            )
+
+    return dest_flat.reshape(src.shape)
 
 
 # def split(img: np.ndarray, half_selector: int, cpar: ControlPar) -> np.ndarray:
@@ -72,7 +206,9 @@ def subtract_img(img1: np.ndarray, img2: np.ndarray, img_new: np.ndarray) -> Non
     img1, img2: numpy arrays containing the original images.
     img_new: numpy array to store the result.
     """
-    img_new[:] = ndimage.maximum(img1 - img2, 0)
+    img_new[:] = np.maximum(img1.astype(np.int16) - img2.astype(np.int16), 0).astype(
+        np.uint8
+    )
 
 
 @njit
@@ -98,33 +234,19 @@ def prepare_image(
 
     filter on an image, optionally followed by additional user-defined filter.
     """
-    # image_size = cpar.imx * cpar.imy
-    # img_lp = np.zeros_like(img, dtype=np.uint8)
-
-    # Apply low-pass filter
-    # img = img.reshape((cpar.imy, cpar.imx))  # Reshape to 2D image
     if img.dtype != np.uint8:
         raise TypeError("Image must be of type uint8")
 
-    img_lp = ndimage.uniform_filter(
-        img,
-        size=dim_lp * 2 + 1,
-        mode="constant",
-        cval=0,
-    )
+    if img.ndim != 2:
+        raise TypeError("Input array must be two-dimensional")
 
-    # Subtract low-pass filtered image from original image
-    img_hp = img | img_lp
+    img_lp = fast_box_blur(dim_lp, img)
+    img_hp = np.empty_like(img)
+    subtract_img(img, img_lp, img_hp)
 
     # Filter highpass image, if wanted, if filter_hp == 0, no highpass filtering
     if filter_hp == 1:
-        img_hp = ndimage.uniform_filter(
-            # img_hp.reshape((cpar.imy, cpar.imx)),
-            img_hp,
-            size=3,
-            mode="constant",
-            cval=0.0,
-        )
+        img_hp = lowpass_3(img_hp)
     elif filter_hp == 2 and filter_file != "":
         try:
             with open(filter_file, "r", encoding="utf-8") as fp:
@@ -132,19 +254,23 @@ def prepare_image(
         except Exception as exc:
             raise IOError(f"Could not open filter file: {filter_file}") from exc
 
-        img_hp = ndimage.convolve(
-            # img_hp.reshape((cpar.imy, cpar.imx)),
-            img_hp,
-            weights=filt,
-            mode="constant",
-            cval=0.0,
-        )
+        img_hp = filter_3(img_hp, filt)
 
     return img_hp
 
 
 def preprocess_image(img, filter_hp, cpar, dim_lp) -> np.ndarray:
     """Decorate prepare_image with default parameters."""
+    if HAS_NATIVE_PREPROCESS:
+        native_cpar = to_native_control_par(cpar)
+        return native_preprocess_image(
+            img,
+            filter_hp,
+            native_cpar,
+            lowpass_dim=dim_lp,
+            filter_file=None,
+            output_img=None,
+        )
     return prepare_image(img=img, dim_lp=dim_lp, filter_hp=filter_hp, filter_file="")
 
 
@@ -235,62 +361,4 @@ def preprocess_image(img, filter_hp, cpar, dim_lp) -> np.ndarray:
 #     pass # replace this with the actual implementation of the function
 
 
-@njit(parallel=True)
-def fast_box_blur_numba(filt_span, src, cpar):
-    imx, imy = cpar["imx"], cpar["imy"]
-    n = 2 * filt_span + 1
-    nq = n * n
-
-    row_accum = np.zeros(imx * imy, dtype=np.int32)
-    col_accum = np.zeros(imx, dtype=np.int32)
-    dest = np.zeros_like(src, dtype=np.int32)
-
-    # Sum over lines first [1]:
-    for i in prange(imy):
-        row_start = i * imx
-        accum = src[row_start]
-        row_accum[row_start] = accum * n
-
-        for j in range(1, filt_span + 1):
-            accum += src[row_start + j - 1] + src[row_start + j + filt_span]
-            row_accum[row_start + j] = accum * n // (2 * j + 1)
-
-        for j in range(filt_span + 1, imx - filt_span):
-            accum += src[row_start + j + filt_span] - src[row_start + j - filt_span - 1]
-            row_accum[row_start + j] = accum
-
-        for j in range(imx - filt_span, imx):
-            accum -= src[row_start + j - filt_span - 1] + src[row_start + j + filt_span]
-            row_accum[row_start + j] = accum * n // (2 * (imx - j - 1) + 1)
-
-    # Sum over columns:
-    col_accum[:imx] = row_accum[:imx]
-    dest[:imx] = col_accum[:imx] // n
-
-    for i in range(1, filt_span + 1):
-        ptr1 = row_accum[(2 * i - 1) * imx : (2 * i + 1) * imx]
-        ptr2 = ptr1[imx:]
-        col_accum += ptr1 + ptr2
-        dest[i * imx : (i + 1) * imx] = n * col_accum // nq // (2 * i + 1)
-
-    for i in range(filt_span + 1, imy - filt_span):
-        ptr1 = row_accum[(i - filt_span - 1) * imx : i * imx]
-        ptr2 = row_accum[(i + filt_span) * imx : (i + filt_span + 1) * imx]
-        col_accum += ptr2 - ptr1
-        dest[i * imx : (i + 1) * imx] = col_accum // nq
-
-    for i in range(filt_span, 0, -1):
-        ptr1 = row_accum[(imy - 2 * i - 1) * imx : (imy - 2 * i + 1) * imx]
-        ptr2 = ptr1[imx:]
-        col_accum -= ptr1 + ptr2
-        dest[(imy - i) * imx :] = n * col_accum // nq // (2 * i + 1)
-
-    return dest
-
-
-# # Example usage:
-# filt_span = 3
-# src = np.random.randint(0, 256, size=(1000, 1000), dtype=np.uint8)
-# cpar = {'imx': src.shape[1], 'imy': src.shape[0]}
-
 # result = fast_box_blur_numba(filt_span, src, cpar)
diff --git a/openptv_python/segmentation.py b/openptv_python/segmentation.py
index 9ae39d7..85c4090 100644
--- a/openptv_python/segmentation.py
+++ b/openptv_python/segmentation.py
@@ -5,6 +5,12 @@
 from numba import njit
 from scipy.ndimage import center_of_mass, gaussian_filter, label, maximum_filter
 
+from ._native_compat import HAS_NATIVE_SEGMENTATION, native_target_recognition
+from ._native_convert import (
+    from_native_target_array,
+    to_native_control_par,
+    to_native_target_par,
+)
 from .constants import CORRES_NONE, MAX_TARGETS
 from .parameters import ControlPar, TargetPar
 from .tracking_frame_buf import Target
@@ -40,6 +46,29 @@ def targ_rec(
     num_cam,
 ) -> List[Target]:
     """Target recognition function."""
+    if (
+        HAS_NATIVE_SEGMENTATION
+        and xmin <= 0
+        and ymin <= 0
+        and xmax >= cpar.imx
+        and ymax >= cpar.imy
+    ):
+        native_tpar = to_native_target_par(targ_par)
+        native_cpar = to_native_control_par(cpar)
+        native_targets = native_target_recognition(
+            img,
+            native_tpar,
+            int(num_cam),
+            native_cpar,
+            subrange_x=None,
+            subrange_y=None,
+        )
+        targets = from_native_target_array(native_targets)
+        threshold = targ_par.gvthresh[int(num_cam)]
+        for target in targets:
+            target.sumg -= target.n * threshold
+        return targets
+
     thres = targ_par.gvthresh[num_cam]
     disco = targ_par.discont
 
diff --git a/plans.md b/plans.md
new file mode 100644
index 0000000..c8885ab
--- /dev/null
+++ b/plans.md
@@ -0,0 +1,2 @@
+openptv-python-2026-03-07-backend-design.md
+openptv-python-2026-03-07-pybind-delegation-map.md
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index 7ddceb8..561046f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,6 +19,7 @@ dependencies = [
   "myst-parser>=5.0.0",
   "numba>=0.64.0",
   "numpy>=2.4.2",
+  "optv>=0.3.2",
   "pre-commit>=4.5.1",
   "pydata-sphinx-theme>=0.16.1",
   "pytest>=9.0.2",
@@ -34,6 +35,7 @@ dynamic = ["version"]
 license = {file = "LICENSE"}
 name = "openptv-python"
 readme = "README.md"
+requires-python = ">=3.12,<3.14"
 
 [tool.coverage.run]
 branch = true
diff --git a/tests/test_image_processing.py b/tests/test_image_processing.py
index c432473..7d6351b 100644
--- a/tests/test_image_processing.py
+++ b/tests/test_image_processing.py
@@ -4,6 +4,12 @@
 
 import numpy as np
 
+import openptv_python.image_processing as image_processing
+from openptv_python._native_compat import (
+    HAS_NATIVE_PREPROCESS,
+    native_preprocess_image,
+)
+from openptv_python._native_convert import to_native_control_par
 from openptv_python.image_processing import prepare_image
 from openptv_python.parameters import ControlPar
 
@@ -38,24 +44,13 @@ def test_arguments(self):
 
     def test_preprocess_image(self):
         """Test that the function returns the correct result."""
-        # correct_res = np.array(
-        #     [
-        #         [0, 0, 0, 0, 0],
-        #         [0, 142, 85, 142, 0],
-        #         [0, 85, 0, 85, 0],
-        #         [0, 142, 85, 142, 0],
-        #         [0, 0, 0, 0, 0],
-        #     ],
-        #     dtype=np.uint8,
-        # )
-
         correct_res = np.array(
             [
-                [28, 56, 85, 56, 28],
-                [56, 255, 255, 255, 56],
-                [85, 255, 255, 255, 85],
-                [56, 255, 255, 255, 56],
-                [28, 56, 85, 56, 28],
+                [0, 0, 0, 0, 0],
+                [0, 142, 85, 142, 0],
+                [0, 85, 0, 85, 0],
+                [0, 142, 85, 142, 0],
+                [0, 0, 0, 0, 0],
             ],
             dtype=np.uint8,
         )
@@ -67,12 +62,46 @@ def test_preprocess_image(self):
             # filter_file='',
         )
 
-        # print(res)
+        np.testing.assert_array_equal(res, correct_res)
+
+    @unittest.skipUnless(
+        HAS_NATIVE_PREPROCESS,
+        "optv native preprocess_image is not available",
+    )
+    def test_preprocess_image_matches_optv_output(self):
+        """The Python and optv preprocessing implementations should agree."""
+        input_img = np.array(
+            [
+                [0, 15, 30, 45, 60, 45, 30],
+                [10, 80, 130, 180, 130, 80, 10],
+                [20, 120, 255, 255, 255, 120, 20],
+                [30, 150, 255, 255, 255, 150, 30],
+                [20, 120, 255, 255, 255, 120, 20],
+                [10, 80, 130, 180, 130, 80, 10],
+                [0, 15, 30, 45, 60, 45, 30],
+            ],
+            dtype=np.uint8,
+        )
+
+        control = ControlPar(1)
+        control.set_image_size((7, 7))
+
+        python_result = image_processing.prepare_image(
+            input_img,
+            dim_lp=1,
+            filter_hp=0,
+            filter_file="",
+        )
+        native_result = native_preprocess_image(
+            input_img,
+            0,
+            to_native_control_par(control),
+            lowpass_dim=1,
+            filter_file=None,
+            output_img=None,
+        )
 
-        # this test fails as we changed the image processing
-        # to use Numpy approach
-        # np.testing.assert_array_equal(res, correct_res)
-        assert np.allclose(res[1:4, 1:4], correct_res[1:4, 1:4])
+        np.testing.assert_array_equal(native_result, python_result)
 
 
 if __name__ == "__main__":
diff --git a/tests/test_native_stress_performance.py b/tests/test_native_stress_performance.py
new file mode 100644
index 0000000..7978afc
--- /dev/null
+++ b/tests/test_native_stress_performance.py
@@ -0,0 +1,288 @@
+"""Stress benchmarks comparing native and non-native backends."""
+
+from __future__ import annotations
+
+import os
+import unittest
+from pathlib import Path
+from statistics import median
+from time import perf_counter
+from unittest.mock import patch
+
+import numpy as np
+
+import openptv_python.image_processing as image_processing
+import openptv_python.orientation as orientation
+import openptv_python.segmentation as segmentation
+from openptv_python._native_compat import (
+    HAS_OPTV,
+    HAS_NATIVE_PREPROCESS,
+    HAS_NATIVE_SEGMENTATION,
+)
+from openptv_python._native_convert import to_native_calibration, to_native_control_par
+from openptv_python.calibration import Calibration
+from openptv_python.imgcoord import image_coordinates
+from openptv_python.parameters import ControlPar, TargetPar, VolumePar
+
+try:
+    from optv.orientation import point_positions as native_point_positions
+    from optv.parameters import VolumeParams as NativeVolumeParams
+
+    HAS_NATIVE_RECONSTRUCTION = True
+except ImportError:
+    native_point_positions = None
+    NativeVolumeParams = None
+    HAS_NATIVE_RECONSTRUCTION = False
+
+
+def _env_flag_enabled(name: str) -> bool:
+    """Interpret common truthy environment variable values."""
+    return os.getenv(name, "").strip().lower() in {"1", "true", "yes", "on"}
+
+
+RUN_STRESS_BENCHMARKS = _env_flag_enabled("OPENPTV_RUN_STRESS_BENCHMARKS")
+
+
+def _benchmark(function, *, warmups: int = 1, runs: int = 3):
+    """Run a callable repeatedly and return the final result and timings."""
+    result = None
+    for _ in range(warmups):
+        result = function()
+
+    timings = []
+    for _ in range(runs):
+        start = perf_counter()
+        result = function()
+        timings.append(perf_counter() - start)
+
+    return result, timings
+
+
+def _timing_summary(label: str, timings: list[float]) -> str:
+    """Return a compact timing summary for benchmark output."""
+    return (
+        f"{label}: median={median(timings):.6f}s "
+        f"min={min(timings):.6f}s max={max(timings):.6f}s"
+    )
+
+
+def _serialize_targets(targets) -> list[tuple[int, float, float, int, int, int, int, int]]:
+    """Convert target objects into stable tuples for comparisons."""
+    return [
+        (
+            int(target.pnr),
+            round(float(target.x), 9),
+            round(float(target.y), 9),
+            int(target.n),
+            int(target.nx),
+            int(target.ny),
+            int(target.sumg),
+            int(target.tnr),
+        )
+        for target in targets
+    ]
+
+
+def _build_segmentation_stress_image(
+    width: int = 512,
+    height: int = 512,
+    spacing: int = 24,
+) -> np.ndarray:
+    """Create a deterministic image with many isolated synthetic targets."""
+    image = np.zeros((height, width), dtype=np.uint8)
+    patch = np.array(
+        [
+            [0, 220, 230, 220, 0],
+            [220, 240, 248, 240, 220],
+            [230, 248, 255, 248, 230],
+            [220, 240, 248, 240, 220],
+            [0, 220, 230, 220, 0],
+        ],
+        dtype=np.uint8,
+    )
+
+    for center_y in range(12, height - 12, spacing):
+        for center_x in range(12, width - 12, spacing):
+            image[center_y - 2 : center_y + 3, center_x - 2 : center_x + 3] = patch
+
+    return image
+
+
+def _build_reconstruction_stress_case(
+    num_points: int = 4096,
+) -> tuple[np.ndarray, np.ndarray, ControlPar, list[Calibration]]:
+    """Build a deterministic multi-camera reconstruction workload."""
+    rng = np.random.default_rng(20260307)
+    points = np.empty((num_points, 3), dtype=np.float64)
+    points[:, 0] = rng.uniform(-25.0, 25.0, size=num_points)
+    points[:, 1] = rng.uniform(20.0, 65.0, size=num_points)
+    points[:, 2] = rng.uniform(-15.0, 15.0, size=num_points)
+
+    cpar = ControlPar(4).from_file(
+        Path("tests/testing_folder/control_parameters/control.par")
+    )
+    cpar.mm.set_n1(1.0)
+    cpar.mm.set_layers([1.0], [1.0])
+    cpar.mm.set_n3(1.0)
+
+    add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
+    calibs = [
+        Calibration().from_file(
+            ori_file=Path(
+                f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"
+            ),
+            add_file=add_file,
+        )
+        for cam_num in range(1, 5)
+    ]
+
+    projections = [image_coordinates(points, cal, cpar.mm) for cal in calibs]
+    targets = np.asarray(projections, dtype=np.float64).transpose(1, 0, 2)
+    return points, targets, cpar, calibs
+
+
+@unittest.skipUnless(
+    RUN_STRESS_BENCHMARKS,
+    "set OPENPTV_RUN_STRESS_BENCHMARKS=1 to run stress benchmarks",
+)
+class TestNativeStressPerformance(unittest.TestCase):
+    """Stress tests comparing native and non-native runtime paths."""
+
+    @unittest.skipUnless(
+        HAS_NATIVE_PREPROCESS,
+        "optv native preprocess_image is not available",
+    )
+    def test_preprocess_image_stress_timing(self):
+        """Compare native preprocessing timing against the Python path."""
+        rng = np.random.default_rng(20260307)
+        image = rng.integers(0, 256, size=(1024, 1024), dtype=np.uint8)
+
+        cpar = ControlPar(1)
+        cpar.set_image_size((image.shape[1], image.shape[0]))
+
+        def python_path():
+            return image_processing.prepare_image(
+                image,
+                dim_lp=1,
+                filter_hp=0,
+                filter_file="",
+            )
+
+        def native_path():
+            return image_processing.preprocess_image(
+                image,
+                filter_hp=0,
+                cpar=cpar,
+                dim_lp=1,
+            )
+
+        python_result, python_timings = _benchmark(python_path)
+        with patch.object(
+            image_processing,
+            "native_preprocess_image",
+            wraps=image_processing.native_preprocess_image,
+        ) as native_call:
+            native_result, native_timings = _benchmark(native_path)
+
+        np.testing.assert_array_equal(native_result, python_result)
+        self.assertGreaterEqual(native_call.call_count, 3)
+
+        speedup = median(python_timings) / median(native_timings)
+        print(
+            "preprocess stress benchmark: "
+            f"{_timing_summary('python', python_timings)}; "
+            f"{_timing_summary('native', native_timings)}; "
+            f"speedup={speedup:.2f}x"
+        )
+
+    @unittest.skipUnless(
+        HAS_NATIVE_SEGMENTATION,
+        "optv native target recognition is not available",
+    )
+    def test_target_recognition_stress_timing(self):
+        """Compare native target recognition timing against Python+Numba."""
+        image = _build_segmentation_stress_image()
+
+        cpar = ControlPar(1)
+        cpar.set_image_size((image.shape[1], image.shape[0]))
+        tpar = TargetPar(
+            gvthresh=[200],
+            discont=20,
+            nnmin=1,
+            nnmax=100,
+            sumg_min=1,
+            nxmin=1,
+            nxmax=10,
+            nymin=1,
+            nymax=10,
+        )
+
+        def python_numba_path():
+            with patch.object(segmentation, "HAS_NATIVE_SEGMENTATION", False):
+                return segmentation.target_recognition(image, tpar, 0, cpar)
+
+        def native_path():
+            return segmentation.target_recognition(image, tpar, 0, cpar)
+
+        python_targets, python_timings = _benchmark(python_numba_path)
+        with patch.object(
+            segmentation,
+            "native_target_recognition",
+            wraps=segmentation.native_target_recognition,
+        ) as native_call:
+            native_targets, native_timings = _benchmark(native_path)
+
+        self.assertEqual(
+            _serialize_targets(native_targets),
+            _serialize_targets(python_targets),
+        )
+        self.assertGreaterEqual(native_call.call_count, 3)
+
+        speedup = median(python_timings) / median(native_timings)
+        print(
+            "segmentation stress benchmark: "
+            f"{_timing_summary('python+numba', python_timings)}; "
+            f"{_timing_summary('native', native_timings)}; "
+            f"speedup={speedup:.2f}x"
+        )
+
+    @unittest.skipUnless(
+        HAS_OPTV and HAS_NATIVE_RECONSTRUCTION,
+        "optv native point_positions is not available",
+    )
+    def test_point_reconstruction_stress_timing(self):
+        """Compare native multi-camera reconstruction timing against Python."""
+        expected_points, targets, cpar, calibs = _build_reconstruction_stress_case()
+        native_cpar = to_native_control_par(cpar)
+        native_cals = [to_native_calibration(cal) for cal in calibs]
+        native_vpar = NativeVolumeParams()
+        python_vpar = VolumePar()
+
+        def python_path():
+            return orientation.point_positions(targets, cpar.mm, calibs, python_vpar)
+
+        def native_path():
+            assert native_point_positions is not None
+            return native_point_positions(targets, native_cpar, native_cals, native_vpar)
+
+        python_result, python_timings = _benchmark(python_path)
+        native_result, native_timings = _benchmark(native_path)
+        python_points, python_rcm = python_result
+        native_points, native_rcm = native_result
+
+        np.testing.assert_allclose(python_points, expected_points, atol=1e-6)
+        np.testing.assert_allclose(native_points, expected_points, atol=1e-6)
+        np.testing.assert_allclose(native_points, python_points, atol=1e-9)
+        np.testing.assert_allclose(native_rcm, python_rcm, atol=1e-9)
+
+        speedup = median(python_timings) / median(native_timings)
+        print(
+            "reconstruction stress benchmark: "
+            f"{_timing_summary('python', python_timings)}; "
+            f"{_timing_summary('native', native_timings)}; "
+            f"speedup={speedup:.2f}x"
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/tests/test_segmentation.py b/tests/test_segmentation.py
index 922d45c..99cfd49 100644
--- a/tests/test_segmentation.py
+++ b/tests/test_segmentation.py
@@ -8,9 +8,12 @@
 """
 
 import unittest
+from unittest.mock import patch
 
 import numpy as np
 
+import openptv_python.segmentation as segmentation
+from openptv_python._native_compat import HAS_NATIVE_SEGMENTATION
 from openptv_python.parameters import ControlPar, TargetPar
 from openptv_python.segmentation import target_recognition
 
@@ -18,6 +21,20 @@
 class TestTargRec(unittest.TestCase):
     """Test the target recognition algorithm."""
 
+    def assert_targets_match(self, native_targets, pure_targets):
+        """Compare target lists field by field to avoid backend coupling."""
+        self.assertEqual(len(native_targets), len(pure_targets))
+
+        for native_target, pure_target in zip(native_targets, pure_targets):
+            self.assertEqual(native_target.pnr, pure_target.pnr)
+            self.assertAlmostEqual(native_target.x, pure_target.x, places=9)
+            self.assertAlmostEqual(native_target.y, pure_target.y, places=9)
+            self.assertEqual(native_target.n, pure_target.n)
+            self.assertEqual(native_target.nx, pure_target.nx)
+            self.assertEqual(native_target.ny, pure_target.ny)
+            self.assertEqual(native_target.sumg, pure_target.sumg)
+            self.assertEqual(native_target.tnr, pure_target.tnr)
+
     def test_single_target(self):
         """Test a single target."""
         img = np.array(
@@ -122,6 +139,54 @@ def test_one_targets2(self):
         self.assertEqual(len(target_array), 1)
         self.assertEqual(target_array[0].count_pixels(), (4, 3, 2))
 
+    @unittest.skipUnless(
+        HAS_NATIVE_SEGMENTATION, "optv native target recognition is not available"
+    )
+    def test_target_recognition_matches_native_backend(self):
+        """Whole-image target detection should call native code and match Python."""
+        img = np.array(
+            [
+                [0, 0, 0, 0, 0, 0, 0],
+                [0, 240, 250, 245, 0, 0, 0],
+                [0, 242, 255, 244, 0, 0, 0],
+                [0, 0, 0, 0, 0, 253, 0],
+                [0, 0, 0, 0, 251, 255, 252],
+                [0, 0, 0, 0, 0, 250, 0],
+                [0, 0, 0, 0, 0, 0, 0],
+            ],
+            dtype=np.uint8,
+        )
+
+        cpar = ControlPar(1)
+        cpar.set_image_size((7, 7))
+        tpar = TargetPar(
+            gvthresh=[239],
+            discont=5,
+            nnmin=1,
+            nnmax=20,
+            sumg_min=1,
+            nxmin=1,
+            nxmax=10,
+            nymin=1,
+            nymax=10,
+        )
+
+        with patch.object(segmentation, "HAS_NATIVE_SEGMENTATION", False):
+            pure_targets = target_recognition(img, tpar, 0, cpar)
+
+        with patch.object(
+            segmentation,
+            "native_target_recognition",
+            wraps=segmentation.native_target_recognition,
+        ) as native_call:
+            native_targets = target_recognition(img, tpar, 0, cpar)
+
+        self.assertTrue(
+            native_call.called, "expected the native implementation to be called"
+        )
+
+        self.assert_targets_match(native_targets, pure_targets)
+
     # def test_peak_fit_new():
     #     """ Test the peak_fit function."""
     #     import matplotlib.pyplot as plt
diff --git a/uv.lock b/uv.lock
index 385e159..0b3e19c 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,4 +1,4 @@
-requires-python = ">=3.14"
+requires-python = ">=3.12, <3.14"
 revision = 3
 version = 1
 
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@@ -436,6 +503,7 @@ dependencies = [
   {name = "myst-parser"},
   {name = "numba"},
   {name = "numpy"},
+  {name = "optv"},
   {name = "pre-commit"},
   {name = "pydata-sphinx-theme"},
   {name = "pytest"},
@@ -455,6 +523,7 @@ requires-dist = [
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   {name = "numba", specifier = ">=0.64.0"},
   {name = "numpy", specifier = ">=2.4.2"},
+  {name = "optv", specifier = ">=0.3.2"},
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   {name = "pydata-sphinx-theme", specifier = ">=0.16.1"},
   {name = "pytest", specifier = ">=9.0.2"},
@@ -466,6 +535,27 @@ requires-dist = [
   {name = "types-pyyaml", specifier = ">=6.0.12.20250915"}
 ]
 
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 [[package]]

From d2ee77ef1bf76a2958655a817764b8f87bdddc71 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 20:00:47 +0200
Subject: [PATCH 15/27] added also tracking

---
 openptv_python/_native_compat.py        |   2 +-
 openptv_python/_native_convert.py       |   4 +-
 openptv_python/tracking_frame_buf.py    |  18 +
 plans.md                                |   2 +-
 tests/test_native_stress_performance.py | 441 +++++++++++++++++++++++-
 tests/test_read_frame.py                |  27 ++
 6 files changed, 481 insertions(+), 13 deletions(-)

diff --git a/openptv_python/_native_compat.py b/openptv_python/_native_compat.py
index fbb01c6..cea39e1 100644
--- a/openptv_python/_native_compat.py
+++ b/openptv_python/_native_compat.py
@@ -66,4 +66,4 @@ def native_preprocess_image(*args: Any, **kwargs: Any) -> Any:
 def native_target_recognition(*args: Any, **kwargs: Any) -> Any:
     if not HAS_NATIVE_SEGMENTATION or optv_segmentation is None:
         raise RuntimeError("optv native target_recognition is not available")
-    return optv_segmentation.target_recognition(*args, **kwargs)
\ No newline at end of file
+    return optv_segmentation.target_recognition(*args, **kwargs)
diff --git a/openptv_python/_native_convert.py b/openptv_python/_native_convert.py
index 001e7ef..949581f 100644
--- a/openptv_python/_native_convert.py
+++ b/openptv_python/_native_convert.py
@@ -2,8 +2,8 @@
 
 from __future__ import annotations
 
-from typing import Iterable, List
 from types import ModuleType
+from typing import Iterable, List
 
 import numpy as np
 
@@ -132,4 +132,4 @@ def from_native_target(native_target) -> Target:
 
 
 def from_native_target_array(native_targets: Iterable[object]) -> List[Target]:
-    return [from_native_target(target) for target in native_targets]
\ No newline at end of file
+    return [from_native_target(target) for target in native_targets]
diff --git a/openptv_python/tracking_frame_buf.py b/openptv_python/tracking_frame_buf.py
index 8ea507c..3539887 100644
--- a/openptv_python/tracking_frame_buf.py
+++ b/openptv_python/tracking_frame_buf.py
@@ -341,6 +341,24 @@ def read(
         frame_num: int,
     ) -> bool:
         """Read a frame from the disk."""
+        required_files = [Path(f"{corres_file_base}.{frame_num}")]
+
+        if linkage_file_base != "":
+            required_files.append(Path(f"{linkage_file_base}.{frame_num}"))
+
+        if prio_file_base != "":
+            required_files.append(Path(f"{prio_file_base}.{frame_num}"))
+
+        for file_base in target_file_base:
+            if frame_num > 0:
+                required_files.append(Path(file_base % frame_num + "_targets"))
+            else:
+                required_files.append(Path(f"{file_base}_targets"))
+
+        for path in required_files:
+            if not path.exists():
+                return False
+
         cor_buf, path_buf = read_path_frame(
             # self.correspond, self.path_info = read_path_frame(
             corres_file_base,
diff --git a/plans.md b/plans.md
index c8885ab..a7521ae 100644
--- a/plans.md
+++ b/plans.md
@@ -1,2 +1,2 @@
 openptv-python-2026-03-07-backend-design.md
-openptv-python-2026-03-07-pybind-delegation-map.md
\ No newline at end of file
+openptv-python-2026-03-07-pybind-delegation-map.md
diff --git a/tests/test_native_stress_performance.py b/tests/test_native_stress_performance.py
index 7978afc..a97c24b 100644
--- a/tests/test_native_stress_performance.py
+++ b/tests/test_native_stress_performance.py
@@ -3,7 +3,10 @@
 from __future__ import annotations
 
 import os
+import shutil
+import tempfile
 import unittest
+from contextlib import contextmanager
 from pathlib import Path
 from statistics import median
 from time import perf_counter
@@ -11,18 +14,35 @@
 
 import numpy as np
 
+import openptv_python.correspondences as correspondences
 import openptv_python.image_processing as image_processing
 import openptv_python.orientation as orientation
 import openptv_python.segmentation as segmentation
 from openptv_python._native_compat import (
-    HAS_OPTV,
     HAS_NATIVE_PREPROCESS,
     HAS_NATIVE_SEGMENTATION,
+    HAS_OPTV,
 )
 from openptv_python._native_convert import to_native_calibration, to_native_control_par
 from openptv_python.calibration import Calibration
-from openptv_python.imgcoord import image_coordinates
-from openptv_python.parameters import ControlPar, TargetPar, VolumePar
+from openptv_python.epi import Coord2d_dtype
+from openptv_python.imgcoord import image_coordinates, img_coord
+from openptv_python.parameters import (
+    ControlPar,
+    TargetPar,
+    VolumePar,
+    read_control_par,
+    read_sequence_par,
+    read_volume_par,
+)
+from openptv_python.track import (
+    track_forward_start,
+    trackcorr_c_finish,
+    trackcorr_c_loop,
+)
+from openptv_python.tracking_frame_buf import Target
+from openptv_python.tracking_run import tr_new
+from openptv_python.trafo import dist_to_flat, metric_to_pixel, pixel_to_metric
 
 try:
     from optv.orientation import point_positions as native_point_positions
@@ -34,6 +54,40 @@
     NativeVolumeParams = None
     HAS_NATIVE_RECONSTRUCTION = False
 
+try:
+    from optv.correspondences import (
+        MatchedCoords as NativeMatchedCoords,
+    )
+    from optv.correspondences import (
+        correspondences as native_correspondences,
+    )
+    from optv.tracking_framebuf import TargetArray as NativeTargetArray
+
+    HAS_NATIVE_STEREOMATCHING = True
+except ImportError:
+    NativeMatchedCoords = None
+    NativeTargetArray = None
+    native_correspondences = None
+    HAS_NATIVE_STEREOMATCHING = False
+
+try:
+    from optv.parameters import (
+        SequenceParams as NativeSequenceParams,
+    )
+    from optv.parameters import (
+        TrackingParams as NativeTrackingParams,
+    )
+    from optv.tracker import Tracker as NativeTracker
+
+    HAS_NATIVE_SEQUENCE = True
+    HAS_NATIVE_TRACKING = NativeVolumeParams is not None
+except ImportError:
+    NativeSequenceParams = None
+    NativeTrackingParams = None
+    NativeTracker = None
+    HAS_NATIVE_SEQUENCE = False
+    HAS_NATIVE_TRACKING = False
+
 
 def _env_flag_enabled(name: str) -> bool:
     """Interpret common truthy environment variable values."""
@@ -66,7 +120,9 @@ def _timing_summary(label: str, timings: list[float]) -> str:
     )
 
 
-def _serialize_targets(targets) -> list[tuple[int, float, float, int, int, int, int, int]]:
+def _serialize_targets(
+    targets,
+) -> list[tuple[int, float, float, int, int, int, int, int]]:
     """Convert target objects into stable tuples for comparisons."""
     return [
         (
@@ -128,9 +184,7 @@ def _build_reconstruction_stress_case(
     add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
     calibs = [
         Calibration().from_file(
-            ori_file=Path(
-                f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"
-            ),
+            ori_file=Path(f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"),
             add_file=add_file,
         )
         for cam_num in range(1, 5)
@@ -141,6 +195,250 @@ def _build_reconstruction_stress_case(
     return points, targets, cpar, calibs
 
 
+def _build_stereomatching_stress_case(
+    grid_width: int = 8,
+    grid_height: int = 8,
+    spacing: float = 5.0,
+):
+    """Build a deterministic multi-camera correspondence workload."""
+    cpar = read_control_par(Path("tests/testing_fodder/parameters/ptv.par"))
+    vpar = read_volume_par(Path("tests/testing_fodder/parameters/criteria.par"))
+    cpar.mm.n2[0] = 1.0001
+    cpar.mm.n3 = 1.0001
+
+    calibs = [
+        Calibration().from_file(
+            ori_file=Path(f"tests/testing_fodder/cal/sym_cam{cam_num}.tif.ori"),
+            add_file=Path("tests/testing_fodder/cal/cam1.tif.addpar"),
+        )
+        for cam_num in range(1, cpar.num_cams + 1)
+    ]
+
+    img_pts: list[list[Target]] = []
+    for cam in range(cpar.num_cams):
+        cam_targets = [Target() for _ in range(grid_width * grid_height)]
+        for row in range(grid_height):
+            for col in range(grid_width):
+                pnr = row * grid_width + col
+                if cam % 2:
+                    pnr = grid_width * grid_height - 1 - pnr
+
+                pos3d = np.array([col * spacing, row * spacing, 0.0], dtype=np.float64)
+                x, y = img_coord(pos3d, calibs[cam], cpar.mm)
+                x, y = metric_to_pixel(x, y, cpar)
+
+                cam_targets[pnr] = Target(
+                    pnr=pnr,
+                    x=float(x),
+                    y=float(y),
+                    n=25,
+                    nx=5,
+                    ny=5,
+                    sumg=10,
+                    tnr=-1,
+                )
+
+        img_pts.append(cam_targets)
+
+    corrected = np.recarray(
+        (cpar.num_cams, len(img_pts[0])),
+        dtype=Coord2d_dtype,
+    )
+    for cam in range(cpar.num_cams):
+        for part, targ in enumerate(img_pts[cam]):
+            x, y = pixel_to_metric(targ.x, targ.y, cpar)
+            x, y = dist_to_flat(x, y, calibs[cam], 0.0001)
+            corrected[cam][part].pnr = targ.pnr
+            corrected[cam][part].x = x
+            corrected[cam][part].y = y
+
+        corrected[cam].sort(order="x")
+
+    return img_pts, corrected, calibs, vpar, cpar
+
+
+def _to_native_target_array(targets: list[Target]):
+    """Convert Python targets into a native TargetArray."""
+    if NativeTargetArray is None:
+        raise RuntimeError("optv TargetArray is not available")
+
+    native_targets = NativeTargetArray(len(targets))
+    for index, target in enumerate(targets):
+        native_target = native_targets[index]
+        native_target.set_pnr(int(target.pnr))
+        native_target.set_tnr(int(target.tnr))
+        native_target.set_pos((float(target.x), float(target.y)))
+        native_target.set_pixel_counts(int(target.n), int(target.nx), int(target.ny))
+        native_target.set_sum_grey_value(int(target.sumg))
+
+    return native_targets
+
+
+def _to_native_volume_par(vpar: VolumePar):
+    """Convert Python volume parameters into native VolumeParams."""
+    if NativeVolumeParams is None:
+        raise RuntimeError("optv VolumeParams is not available")
+
+    native_vpar = NativeVolumeParams()
+    native_vpar.set_X_lay(list(vpar.x_lay))
+    native_vpar.set_Zmin_lay(list(vpar.z_min_lay))
+    native_vpar.set_Zmax_lay(list(vpar.z_max_lay))
+    native_vpar.set_cn(vpar.cn)
+    native_vpar.set_cnx(vpar.cnx)
+    native_vpar.set_cny(vpar.cny)
+    native_vpar.set_csumg(vpar.csumg)
+    native_vpar.set_eps0(vpar.eps0)
+    native_vpar.set_corrmin(vpar.corrmin)
+    return native_vpar
+
+
+def _normalize_correspondence_output(sorted_pos, sorted_corresp):
+    """Sort correspondence outputs into a stable order for comparisons."""
+    normalized_pos = []
+    normalized_corresp = []
+
+    for positions, identifiers in zip(sorted_pos, sorted_corresp):
+        if identifiers.shape[1] == 0:
+            normalized_pos.append(positions)
+            normalized_corresp.append(identifiers)
+            continue
+
+        sortable_ids = np.where(identifiers < 0, np.iinfo(np.int64).max, identifiers)
+        order = np.lexsort(sortable_ids[::-1])
+        normalized_pos.append(positions[:, order, :])
+        normalized_corresp.append(identifiers[:, order])
+
+    return normalized_pos, normalized_corresp
+
+
+@contextmanager
+def _working_directory(path: Path):
+    """Temporarily change the current working directory."""
+    previous = Path.cwd()
+    os.chdir(path)
+    try:
+        yield
+    finally:
+        os.chdir(previous)
+
+
+def _read_track_calibrations(workdir: Path, num_cams: int) -> list[Calibration]:
+    """Read all tracking calibrations from a copied tracking fixture."""
+    return [
+        Calibration().from_file(
+            ori_file=workdir / f"cal/cam{cam_num}.tif.ori",
+            add_file=workdir / f"cal/cam{cam_num}.tif.addpar",
+        )
+        for cam_num in range(1, num_cams + 1)
+    ]
+
+
+def _snapshot_text_outputs(root: Path) -> dict[str, tuple[int, str]]:
+    """Summarize benchmark output files into a stable comparison dictionary."""
+    return {
+        str(path.relative_to(root)): (
+            len(path.read_text(encoding="utf-8").splitlines()),
+            path.read_text(encoding="utf-8").splitlines()[0]
+            if path.read_text(encoding="utf-8").splitlines()
+            else "",
+        )
+        for path in sorted(root.rglob("*"))
+        if path.is_file()
+    }
+
+
+def _run_python_tracking_fixture() -> dict[str, tuple[int, str]]:
+    """Execute the Python tracking loop in a temporary fixture directory."""
+    source = Path("tests/testing_fodder/track")
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        workdir = Path(tmp_dir) / "track"
+        shutil.copytree(source, workdir)
+        shutil.copytree(workdir / "img_orig", workdir / "img")
+        output_dir = workdir / "py_res"
+        shutil.copytree(workdir / "res_orig", output_dir)
+
+        with _working_directory(workdir):
+            cpar = read_control_par(Path("parameters/ptv.par"))
+            calibs = _read_track_calibrations(workdir, cpar.num_cams)
+            run = tr_new(
+                Path("parameters/sequence.par"),
+                Path("parameters/track.par"),
+                Path("parameters/criteria.par"),
+                Path("parameters/ptv.par"),
+                4,
+                20000,
+                "py_res/rt_is",
+                "py_res/ptv_is",
+                "py_res/added",
+                calibs,
+                10000.0,
+            )
+            run.seq_par.first = 10240
+            run.seq_par.last = 10250
+            run.tpar = run.tpar._replace(add=1)
+
+            track_forward_start(run)
+            trackcorr_c_loop(run, run.seq_par.first)
+            for step in range(run.seq_par.first + 1, run.seq_par.last):
+                trackcorr_c_loop(run, step)
+            trackcorr_c_finish(run, run.seq_par.last)
+
+        return _snapshot_text_outputs(output_dir)
+
+
+def _run_native_tracking_fixture() -> dict[str, tuple[int, str]]:
+    """Execute the native tracker in a temporary fixture directory."""
+    if (
+        NativeTracker is None
+        or NativeSequenceParams is None
+        or NativeTrackingParams is None
+    ):
+        raise RuntimeError("optv Tracker is not available")
+
+    source = Path("tests/testing_fodder/track")
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        workdir = Path(tmp_dir) / "track"
+        shutil.copytree(source, workdir)
+        shutil.copytree(workdir / "img_orig", workdir / "img")
+        output_dir = workdir / "native_res"
+        shutil.copytree(workdir / "res_orig", output_dir)
+
+        with _working_directory(workdir):
+            py_cpar = read_control_par(Path("parameters/ptv.par"))
+            py_vpar = read_volume_par(Path("parameters/criteria.par"))
+            calibs = _read_track_calibrations(workdir, py_cpar.num_cams)
+
+            native_cpar = to_native_control_par(py_cpar)
+            native_vpar = _to_native_volume_par(py_vpar)
+            native_tpar = NativeTrackingParams()
+            native_tpar.read_track_par("parameters/track.par")
+            native_tpar.set_add(1)
+
+            native_spar = NativeSequenceParams(num_cams=py_cpar.num_cams)
+            native_spar.read_sequence_par("parameters/sequence.par", py_cpar.num_cams)
+            native_spar.set_first(10240)
+            native_spar.set_last(10250)
+            for cam, img_base_name in enumerate(py_cpar.img_base_name):
+                native_spar.set_img_base_name(cam, img_base_name.replace("%05d", ""))
+
+            tracker = NativeTracker(
+                native_cpar,
+                native_vpar,
+                native_tpar,
+                native_spar,
+                [to_native_calibration(cal) for cal in calibs],
+                naming={
+                    "corres": "native_res/rt_is",
+                    "linkage": "native_res/ptv_is",
+                    "prio": "native_res/added",
+                },
+                flatten_tol=10000.0,
+            )
+            tracker.full_forward()
+
+        return _snapshot_text_outputs(output_dir)
+
+
 @unittest.skipUnless(
     RUN_STRESS_BENCHMARKS,
     "set OPENPTV_RUN_STRESS_BENCHMARKS=1 to run stress benchmarks",
@@ -148,6 +446,41 @@ def _build_reconstruction_stress_case(
 class TestNativeStressPerformance(unittest.TestCase):
     """Stress tests comparing native and non-native runtime paths."""
 
+    @unittest.skipUnless(
+        HAS_NATIVE_SEQUENCE,
+        "optv native SequenceParams is not available",
+    )
+    def test_sequence_parameter_read_stress_timing(self):
+        """Compare sequence parameter loading timing against native bindings."""
+        sequence_file = Path("tests/testing_folder/sequence_parameters/sequence.par")
+
+        def python_path():
+            return read_sequence_par(sequence_file, 4)
+
+        def native_path():
+            assert NativeSequenceParams is not None
+            seq = NativeSequenceParams(num_cams=4)
+            seq.read_sequence_par(str(sequence_file), 4)
+            return seq
+
+        python_result, python_timings = _benchmark(python_path, runs=5)
+        native_result, native_timings = _benchmark(native_path, runs=5)
+
+        self.assertEqual(python_result.first, native_result.get_first())
+        self.assertEqual(python_result.last, native_result.get_last())
+        self.assertEqual(
+            python_result.img_base_name,
+            [native_result.get_img_base_name(cam) for cam in range(4)],
+        )
+
+        speedup = median(python_timings) / median(native_timings)
+        print(
+            "sequence stress benchmark: "
+            f"{_timing_summary('python', python_timings)}; "
+            f"{_timing_summary('native', native_timings)}; "
+            f"speedup={speedup:.2f}x"
+        )
+
     @unittest.skipUnless(
         HAS_NATIVE_PREPROCESS,
         "optv native preprocess_image is not available",
@@ -246,6 +579,67 @@ def native_path():
             f"speedup={speedup:.2f}x"
         )
 
+    @unittest.skipUnless(
+        HAS_NATIVE_STEREOMATCHING,
+        "optv native correspondences is not available",
+    )
+    def test_stereomatching_stress_timing(self):
+        """Compare native correspondences timing against the Python path."""
+        img_pts, corrected, calibs, vpar, cpar = _build_stereomatching_stress_case()
+        native_cpar = to_native_control_par(cpar)
+        native_vpar = _to_native_volume_par(vpar)
+        native_cals = [to_native_calibration(cal) for cal in calibs]
+        native_img_pts = [_to_native_target_array(targets) for targets in img_pts]
+        native_flat_coords = [
+            NativeMatchedCoords(
+                native_img_pts[cam], native_cpar, native_cals[cam], 0.0001
+            )
+            for cam in range(cpar.num_cams)
+        ]
+
+        def python_path():
+            return correspondences.py_correspondences(
+                img_pts, corrected, calibs, vpar, cpar
+            )
+
+        def native_path():
+            assert native_correspondences is not None
+            return native_correspondences(
+                native_img_pts,
+                native_flat_coords,
+                native_cals,
+                native_vpar,
+                native_cpar,
+            )
+
+        python_result, python_timings = _benchmark(python_path)
+        native_result, native_timings = _benchmark(native_path)
+        python_pos, python_corresp, python_num_targs = python_result
+        native_pos, native_corresp, native_num_targs = native_result
+        python_pos, python_corresp = _normalize_correspondence_output(
+            python_pos,
+            python_corresp,
+        )
+        native_pos, native_corresp = _normalize_correspondence_output(
+            native_pos,
+            native_corresp,
+        )
+
+        self.assertEqual(python_num_targs, native_num_targs)
+        self.assertEqual(len(python_corresp), len(native_corresp))
+        for python_positions, native_positions in zip(python_pos, native_pos):
+            np.testing.assert_allclose(native_positions, python_positions, atol=1e-9)
+        for python_ids, native_ids in zip(python_corresp, native_corresp):
+            np.testing.assert_array_equal(native_ids, python_ids)
+
+        speedup = median(python_timings) / median(native_timings)
+        print(
+            "stereomatching stress benchmark: "
+            f"{_timing_summary('python', python_timings)}; "
+            f"{_timing_summary('native', native_timings)}; "
+            f"speedup={speedup:.2f}x"
+        )
+
     @unittest.skipUnless(
         HAS_OPTV and HAS_NATIVE_RECONSTRUCTION,
         "optv native point_positions is not available",
@@ -263,7 +657,9 @@ def python_path():
 
         def native_path():
             assert native_point_positions is not None
-            return native_point_positions(targets, native_cpar, native_cals, native_vpar)
+            return native_point_positions(
+                targets, native_cpar, native_cals, native_vpar
+            )
 
         python_result, python_timings = _benchmark(python_path)
         native_result, native_timings = _benchmark(native_path)
@@ -283,6 +679,33 @@ def native_path():
             f"speedup={speedup:.2f}x"
         )
 
+    @unittest.skipUnless(
+        HAS_NATIVE_TRACKING,
+        "optv native Tracker is not available",
+    )
+    def test_tracking_sequence_stress_timing(self):
+        """Compare native tracking over a short sequence against Python."""
+
+        def python_path():
+            return _run_python_tracking_fixture()
+
+        def native_path():
+            return _run_native_tracking_fixture()
+
+        python_outputs, python_timings = _benchmark(python_path, warmups=0, runs=1)
+        native_outputs, native_timings = _benchmark(native_path, warmups=0, runs=1)
+
+        self.assertTrue(python_outputs)
+        self.assertEqual(native_outputs, python_outputs)
+
+        speedup = median(python_timings) / median(native_timings)
+        print(
+            "tracking stress benchmark: "
+            f"{_timing_summary('python', python_timings)}; "
+            f"{_timing_summary('native', native_timings)}; "
+            f"speedup={speedup:.2f}x"
+        )
+
 
 if __name__ == "__main__":
-    unittest.main()
\ No newline at end of file
+    unittest.main()
diff --git a/tests/test_read_frame.py b/tests/test_read_frame.py
index 430482e..6409a38 100644
--- a/tests/test_read_frame.py
+++ b/tests/test_read_frame.py
@@ -1,6 +1,9 @@
 """Test the Frame class."""
 
+import shutil
+import tempfile
 import unittest
+from pathlib import Path
 
 import numpy as np
 
@@ -62,6 +65,30 @@ def test_read_frame(self):
         )
         np.testing.assert_array_equal(targs, targs_correct)
 
+    def test_read_frame_returns_false_when_linkage_file_missing(self):
+        """Missing linkage data must fail the frame read instead of producing an empty frame."""
+        source_dir = Path("tests/testing_folder/frame")
+
+        with tempfile.TemporaryDirectory() as temp_dir:
+            work_dir = Path(temp_dir)
+            for path in source_dir.iterdir():
+                shutil.copy2(path, work_dir / path.name)
+
+            (work_dir / "ptv_is.333").unlink()
+
+            targ_files = [str(work_dir / f"cam{c:d}.%04d") for c in range(1, 5)]
+            frm = Frame(num_cams=4)
+
+            self.assertFalse(
+                frm.read(
+                    corres_file_base=str(work_dir / "rt_is"),
+                    linkage_file_base=str(work_dir / "ptv_is"),
+                    prio_file_base=str(work_dir / "added"),
+                    target_file_base=targ_files,
+                    frame_num=333,
+                )
+            )
+
 
 if __name__ == "__main__":
     unittest.main()

From 36624bba45438e5ed600e6df2276ad0480b4051f Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 22:17:41 +0200
Subject: [PATCH 16/27] documentation

---
 README.html                             | 679 ++++++++++++++++++++++++
 README.md                               | 129 ++++-
 docs/index.md                           |   9 +
 tests/test_native_stress_performance.py |   4 +-
 4 files changed, 818 insertions(+), 3 deletions(-)
 create mode 100644 README.html

diff --git a/README.html b/README.html
new file mode 100644
index 0000000..8096b12
--- /dev/null
+++ b/README.html
@@ -0,0 +1,679 @@
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
+
+<meta charset="utf-8">
+<meta name="generator" content="quarto-1.7.32">
+
+<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
+
+
+<title>readme</title>
+<style>
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+div.columns{display: flex; gap: min(4vw, 1.5em);}
+div.column{flex: auto; overflow-x: auto;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
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+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
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+    -khtml-user-select: none; -moz-user-select: none;
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+    padding: 0 4px; width: 4em;
+  }
+pre.numberSource { margin-left: 3em;  padding-left: 4px; }
+div.sourceCode
+  {   }
+@media screen {
+pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
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+</style>
+
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+<script src="README_files/libs/quarto-html/tippy.umd.min.js"></script>
+<script src="README_files/libs/quarto-html/anchor.min.js"></script>
+<link href="README_files/libs/quarto-html/tippy.css" rel="stylesheet">
+<link href="README_files/libs/quarto-html/quarto-syntax-highlighting-37eea08aefeeee20ff55810ff984fec1.css" rel="stylesheet" id="quarto-text-highlighting-styles">
+<script src="README_files/libs/bootstrap/bootstrap.min.js"></script>
+<link href="README_files/libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
+<link href="README_files/libs/bootstrap/bootstrap-6140de385eaf1dff3775f86cf5bcc5bc.min.css" rel="stylesheet" append-hash="true" id="quarto-bootstrap" data-mode="light">
+
+
+</head>
+
+<body class="fullcontent quarto-light">
+
+<div id="quarto-content" class="page-columns page-rows-contents page-layout-article">
+
+<main class="content" id="quarto-document-content">
+
+
+
+
+<section id="openptv-python" class="level1">
+<h1>openptv-python</h1>
+<p>Python version of the OpenPTV library - this is <em>a work in progress</em></p>
+<section id="what-this-repo-provides" class="level2">
+<h2 class="anchored" data-anchor-id="what-this-repo-provides">What This Repo Provides</h2>
+<p><code>openptv-python</code> keeps the Python API as the main interface and combines three execution modes behind that API:</p>
+<ul>
+<li>Pure Python: the reference implementation and the easiest path for reading, debugging, and extending the code.</li>
+<li>Python + Numba: several hot kernels are JIT-compiled automatically on first use, so the Python implementation still benefits from acceleration.</li>
+<li>Native <code>optv</code> bindings: selected operations reuse the native OpenPTV implementation when the <code>optv</code> package is available.</li>
+</ul>
+<p>At the moment, automatic native delegation is implemented for image preprocessing and full-frame target recognition. The rest of the library keeps the same Python API and remains usable even when those native paths are not in use.</p>
+</section>
+<section id="how-this-is-started" class="level2">
+<h2 class="anchored" data-anchor-id="how-this-is-started">How this is started</h2>
+<p>This work started from the https://github.com/OpenPTV/openptv/tree/pure_python branch. It’s a long-standing idea to convert all the C code to Python and now it’s possible with ChatGPT to save a lot of typing time.</p>
+<p>This repo is created using a <em>cookiecutter</em> and the rest of the readme describes the way to work with this structure</p>
+</section>
+<section id="supported-python-versions" class="level2">
+<h2 class="anchored" data-anchor-id="supported-python-versions">Supported Python Versions</h2>
+<p>The project currently supports Python <code>&gt;=3.12,&lt;3.14</code>.</p>
+</section>
+<section id="installation" class="level2">
+<h2 class="anchored" data-anchor-id="installation">Installation</h2>
+<section id="recommended-uv" class="level3">
+<h3 class="anchored" data-anchor-id="recommended-uv">Recommended: uv</h3>
+<p>Create the environment and install all project dependencies:</p>
+<div class="sourceCode" id="cb1"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="ex">uv</span> venv</span>
+<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="bu">source</span> .venv/bin/activate</span>
+<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="ex">uv</span> sync</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<p>This is the simplest way to get the current default stack, including NumPy, SciPy, Numba, and the <code>optv</code> native bindings when they are available for your platform and Python version.</p>
+</section>
+<section id="alternative-conda-pip" class="level3">
+<h3 class="anchored" data-anchor-id="alternative-conda-pip">Alternative: conda + pip</h3>
+<div class="sourceCode" id="cb2"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="ex">conda</span> create <span class="at">-n</span> openptv-python <span class="at">-c</span> conda-forge python=3.12</span>
+<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="ex">conda</span> activate openptv-python</span>
+<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="ex">pip</span> install <span class="at">-e</span> .</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</section>
+<section id="what-gets-installed" class="level3">
+<h3 class="anchored" data-anchor-id="what-gets-installed">What gets installed</h3>
+<ul>
+<li><code>numba</code> is part of the default dependency set and accelerates selected Python kernels automatically after the first call.</li>
+<li><code>optv</code> is part of the default dependency set and enables native interop on supported platforms.</li>
+<li>The public API stays the same regardless of which backend is active.</li>
+</ul>
+</section>
+</section>
+<section id="backend-behavior" class="level2">
+<h2 class="anchored" data-anchor-id="backend-behavior">Backend Behavior</h2>
+<section id="pure-python-backend" class="level3">
+<h3 class="anchored" data-anchor-id="pure-python-backend">Pure Python backend</h3>
+<p>This is the base implementation for the whole library. It is always the source of truth for the Python API and remains the fallback behavior for code paths that are not delegated to <code>optv</code>.</p>
+</section>
+<section id="python-numba-backend" class="level3">
+<h3 class="anchored" data-anchor-id="python-numba-backend">Python + Numba backend</h3>
+<p>Numba accelerates selected computational kernels inside the Python implementation. This is automatic; there is no separate API to enable it. Expect the first call to a JIT-compiled function to be slower due to compilation, with later calls running faster.</p>
+</section>
+<section id="native-optv-backend" class="level3">
+<h3 class="anchored" data-anchor-id="native-optv-backend">Native <code>optv</code> backend</h3>
+<p>When <code>optv</code> imports successfully, <code>openptv-python</code> automatically reuses native implementations for:</p>
+<ul>
+<li>image preprocessing</li>
+<li>full-frame target recognition / segmentation</li>
+</ul>
+<p>These native paths are validated against the Python implementation by parity tests, so results stay backend-independent.</p>
+</section>
+<section id="backend-capability-table" class="level3">
+<h3 class="anchored" data-anchor-id="backend-capability-table">Backend Capability Table</h3>
+<table class="caption-top table">
+<colgroup>
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+<col style="width: 25%">
+</colgroup>
+<thead>
+<tr class="header">
+<th>Operation</th>
+<th>Pure Python</th>
+<th>Python + Numba</th>
+<th>Native <code>optv</code></th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td>Image preprocessing</td>
+<td>Yes</td>
+<td>Yes</td>
+<td>Yes, automatic delegation</td>
+</tr>
+<tr class="even">
+<td>Target recognition / segmentation</td>
+<td>Yes</td>
+<td>Yes</td>
+<td>Yes, automatic delegation</td>
+</tr>
+<tr class="odd">
+<td>Point reconstruction</td>
+<td>Yes</td>
+<td>Partial internal kernels</td>
+<td>Not used by default</td>
+</tr>
+<tr class="even">
+<td>Correspondence search / stereo matching</td>
+<td>Yes</td>
+<td>Partial internal kernels</td>
+<td>Not used by default</td>
+</tr>
+<tr class="odd">
+<td>Tracking</td>
+<td>Yes</td>
+<td>Partial internal kernels</td>
+<td>Not used by default</td>
+</tr>
+<tr class="even">
+<td>Sequence parameter I/O</td>
+<td>Yes</td>
+<td>No</td>
+<td>Available in native bindings</td>
+</tr>
+</tbody>
+</table>
+<p><code>Not used by default</code> means the native path exists in benchmarks or conversion helpers, but the regular <code>openptv-python</code> runtime path still uses the Python implementation unless that operation is explicitly integrated later.</p>
+</section>
+</section>
+<section id="getting-started" class="level2">
+<h2 class="anchored" data-anchor-id="getting-started">Getting Started</h2>
+<section id="install-the-project" class="level3">
+<h3 class="anchored" data-anchor-id="install-the-project">1. Install the project</h3>
+<p>Use one of the installation methods above.</p>
+</section>
+<section id="verify-imports" class="level3">
+<h3 class="anchored" data-anchor-id="verify-imports">2. Verify imports</h3>
+<div class="sourceCode" id="cb3"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="ex">uv</span> run python <span class="at">-</span> <span class="op">&lt;&lt;'PY'</span></span>
+<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="st">import openptv_python</span></span>
+<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="st">import numba</span></span>
+<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a><span class="st">import optv</span></span>
+<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="st">print("openptv_python ok")</span></span>
+<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="st">print("numba ok", numba.__version__)</span></span>
+<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="st">print("optv ok", optv.__version__)</span></span>
+<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="op">PY</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</section>
+<section id="start-using-the-python-api" class="level3">
+<h3 class="anchored" data-anchor-id="start-using-the-python-api">3. Start using the Python API</h3>
+<div class="sourceCode" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="op">&gt;&gt;&gt;</span> <span class="im">import</span> openptv_python</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</section>
+<section id="run-the-test-suite" class="level3">
+<h3 class="anchored" data-anchor-id="run-the-test-suite">4. Run the test suite</h3>
+<div class="sourceCode" id="cb5"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="ex">uv</span> run make</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<p>Stress and performance tests are part of the default suite now. If you need a faster validation pass locally, you can skip them explicitly:</p>
+<div class="sourceCode" id="cb6"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="va">OPENPTV_SKIP_STRESS_BENCHMARKS</span><span class="op">=</span>1 <span class="ex">uv</span> run make</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</section>
+<section id="workflow-for-developerscontributors" class="level3">
+<h3 class="anchored" data-anchor-id="workflow-for-developerscontributors">Workflow for developers/contributors</h3>
+<p>For the best experience create a new conda environment (e.g.&nbsp;DEVELOP) with Python 3.12:</p>
+<pre><code>conda create -n openptv-python -c conda-forge python=3.12
+conda activate openptv-python</code></pre>
+<p>Before pushing to GitHub, run the following commands:</p>
+<ol type="1">
+<li>Update conda environment: <code>make conda-env-update</code> or <code>uv venv</code> and <code>source .venv/bin/activate</code> followed by <code>uv sync --upgrade</code></li>
+<li>Install this package: <code>pip install -e .</code></li>
+<li>Sync with the latest <a href="https://github.com/ecmwf-projects/cookiecutter-conda-package">template</a> (optional): <code>make template-update</code></li>
+<li>Run quality assurance checks: <code>make qa</code></li>
+<li>Run tests: <code>make unit-tests</code></li>
+<li>Run the static type checker: <code>make type-check</code></li>
+<li>Build the documentation (see <a href="https://www.sphinx-doc.org/en/master/tutorial/">Sphinx tutorial</a>): <code>make docs-build</code></li>
+</ol>
+</section>
+<section id="license" class="level3">
+<h3 class="anchored" data-anchor-id="license">License</h3>
+<pre><code>Copyright 2023, OpenPTV consortium.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.</code></pre>
+</section>
+</section>
+</section>
+
+</main>
+<!-- /main column -->
+<script id="quarto-html-after-body" type="application/javascript">
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+        if (note.children && note.children.length > 2) {
+          container.appendChild(note.children[0].cloneNode(true));
+          for (let i = 1; i < note.children.length; i++) {
+            const child = note.children[i];
+            if (child.tagName === "P" && child.innerText === "") {
+              continue;
+            } else {
+              container.appendChild(child.cloneNode(true));
+              break;
+            }
+          }
+          if (window.Quarto?.typesetMath) {
+            window.Quarto.typesetMath(container);
+          }
+          return container.innerHTML
+        } else {
+          if (window.Quarto?.typesetMath) {
+            window.Quarto.typesetMath(note);
+          }
+          return note.innerHTML;
+        }
+      } else {
+        // Remove any anchor links if they are present
+        const anchorLink = note.querySelector('a.anchorjs-link');
+        if (anchorLink) {
+          anchorLink.remove();
+        }
+        if (window.Quarto?.typesetMath) {
+          window.Quarto.typesetMath(note);
+        }
+        if (note.classList.contains("callout")) {
+          return note.outerHTML;
+        } else {
+          return note.innerHTML;
+        }
+      }
+    }
+    for (var i=0; i<xrefs.length; i++) {
+      const xref = xrefs[i];
+      tippyHover(xref, undefined, function(instance) {
+        instance.disable();
+        let url = xref.getAttribute('href');
+        let hash = undefined; 
+        if (url.startsWith('#')) {
+          hash = url;
+        } else {
+          try { hash = new URL(url).hash; } catch {}
+        }
+        if (hash) {
+          const id = hash.replace(/^#\/?/, "");
+          const note = window.document.getElementById(id);
+          if (note !== null) {
+            try {
+              const html = processXRef(id, note.cloneNode(true));
+              instance.setContent(html);
+            } finally {
+              instance.enable();
+              instance.show();
+            }
+          } else {
+            // See if we can fetch this
+            fetch(url.split('#')[0])
+            .then(res => res.text())
+            .then(html => {
+              const parser = new DOMParser();
+              const htmlDoc = parser.parseFromString(html, "text/html");
+              const note = htmlDoc.getElementById(id);
+              if (note !== null) {
+                const html = processXRef(id, note);
+                instance.setContent(html);
+              } 
+            }).finally(() => {
+              instance.enable();
+              instance.show();
+            });
+          }
+        } else {
+          // See if we can fetch a full url (with no hash to target)
+          // This is a special case and we should probably do some content thinning / targeting
+          fetch(url)
+          .then(res => res.text())
+          .then(html => {
+            const parser = new DOMParser();
+            const htmlDoc = parser.parseFromString(html, "text/html");
+            const note = htmlDoc.querySelector('main.content');
+            if (note !== null) {
+              // This should only happen for chapter cross references
+              // (since there is no id in the URL)
+              // remove the first header
+              if (note.children.length > 0 && note.children[0].tagName === "HEADER") {
+                note.children[0].remove();
+              }
+              const html = processXRef(null, note);
+              instance.setContent(html);
+            } 
+          }).finally(() => {
+            instance.enable();
+            instance.show();
+          });
+        }
+      }, function(instance) {
+      });
+    }
+        let selectedAnnoteEl;
+        const selectorForAnnotation = ( cell, annotation) => {
+          let cellAttr = 'data-code-cell="' + cell + '"';
+          let lineAttr = 'data-code-annotation="' +  annotation + '"';
+          const selector = 'span[' + cellAttr + '][' + lineAttr + ']';
+          return selector;
+        }
+        const selectCodeLines = (annoteEl) => {
+          const doc = window.document;
+          const targetCell = annoteEl.getAttribute("data-target-cell");
+          const targetAnnotation = annoteEl.getAttribute("data-target-annotation");
+          const annoteSpan = window.document.querySelector(selectorForAnnotation(targetCell, targetAnnotation));
+          const lines = annoteSpan.getAttribute("data-code-lines").split(",");
+          const lineIds = lines.map((line) => {
+            return targetCell + "-" + line;
+          })
+          let top = null;
+          let height = null;
+          let parent = null;
+          if (lineIds.length > 0) {
+              //compute the position of the single el (top and bottom and make a div)
+              const el = window.document.getElementById(lineIds[0]);
+              top = el.offsetTop;
+              height = el.offsetHeight;
+              parent = el.parentElement.parentElement;
+            if (lineIds.length > 1) {
+              const lastEl = window.document.getElementById(lineIds[lineIds.length - 1]);
+              const bottom = lastEl.offsetTop + lastEl.offsetHeight;
+              height = bottom - top;
+            }
+            if (top !== null && height !== null && parent !== null) {
+              // cook up a div (if necessary) and position it 
+              let div = window.document.getElementById("code-annotation-line-highlight");
+              if (div === null) {
+                div = window.document.createElement("div");
+                div.setAttribute("id", "code-annotation-line-highlight");
+                div.style.position = 'absolute';
+                parent.appendChild(div);
+              }
+              div.style.top = top - 2 + "px";
+              div.style.height = height + 4 + "px";
+              div.style.left = 0;
+              let gutterDiv = window.document.getElementById("code-annotation-line-highlight-gutter");
+              if (gutterDiv === null) {
+                gutterDiv = window.document.createElement("div");
+                gutterDiv.setAttribute("id", "code-annotation-line-highlight-gutter");
+                gutterDiv.style.position = 'absolute';
+                const codeCell = window.document.getElementById(targetCell);
+                const gutter = codeCell.querySelector('.code-annotation-gutter');
+                gutter.appendChild(gutterDiv);
+              }
+              gutterDiv.style.top = top - 2 + "px";
+              gutterDiv.style.height = height + 4 + "px";
+            }
+            selectedAnnoteEl = annoteEl;
+          }
+        };
+        const unselectCodeLines = () => {
+          const elementsIds = ["code-annotation-line-highlight", "code-annotation-line-highlight-gutter"];
+          elementsIds.forEach((elId) => {
+            const div = window.document.getElementById(elId);
+            if (div) {
+              div.remove();
+            }
+          });
+          selectedAnnoteEl = undefined;
+        };
+          // Handle positioning of the toggle
+      window.addEventListener(
+        "resize",
+        throttle(() => {
+          elRect = undefined;
+          if (selectedAnnoteEl) {
+            selectCodeLines(selectedAnnoteEl);
+          }
+        }, 10)
+      );
+      function throttle(fn, ms) {
+      let throttle = false;
+      let timer;
+        return (...args) => {
+          if(!throttle) { // first call gets through
+              fn.apply(this, args);
+              throttle = true;
+          } else { // all the others get throttled
+              if(timer) clearTimeout(timer); // cancel #2
+              timer = setTimeout(() => {
+                fn.apply(this, args);
+                timer = throttle = false;
+              }, ms);
+          }
+        };
+      }
+        // Attach click handler to the DT
+        const annoteDls = window.document.querySelectorAll('dt[data-target-cell]');
+        for (const annoteDlNode of annoteDls) {
+          annoteDlNode.addEventListener('click', (event) => {
+            const clickedEl = event.target;
+            if (clickedEl !== selectedAnnoteEl) {
+              unselectCodeLines();
+              const activeEl = window.document.querySelector('dt[data-target-cell].code-annotation-active');
+              if (activeEl) {
+                activeEl.classList.remove('code-annotation-active');
+              }
+              selectCodeLines(clickedEl);
+              clickedEl.classList.add('code-annotation-active');
+            } else {
+              // Unselect the line
+              unselectCodeLines();
+              clickedEl.classList.remove('code-annotation-active');
+            }
+          });
+        }
+    const findCites = (el) => {
+      const parentEl = el.parentElement;
+      if (parentEl) {
+        const cites = parentEl.dataset.cites;
+        if (cites) {
+          return {
+            el,
+            cites: cites.split(' ')
+          };
+        } else {
+          return findCites(el.parentElement)
+        }
+      } else {
+        return undefined;
+      }
+    };
+    var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
+    for (var i=0; i<bibliorefs.length; i++) {
+      const ref = bibliorefs[i];
+      const citeInfo = findCites(ref);
+      if (citeInfo) {
+        tippyHover(citeInfo.el, function() {
+          var popup = window.document.createElement('div');
+          citeInfo.cites.forEach(function(cite) {
+            var citeDiv = window.document.createElement('div');
+            citeDiv.classList.add('hanging-indent');
+            citeDiv.classList.add('csl-entry');
+            var biblioDiv = window.document.getElementById('ref-' + cite);
+            if (biblioDiv) {
+              citeDiv.innerHTML = biblioDiv.innerHTML;
+            }
+            popup.appendChild(citeDiv);
+          });
+          return popup.innerHTML;
+        });
+      }
+    }
+  });
+  </script>
+</div> <!-- /content -->
+
+
+
+
+</body></html>
\ No newline at end of file
diff --git a/README.md b/README.md
index 1a873da..c2b7bbc 100644
--- a/README.md
+++ b/README.md
@@ -2,6 +2,23 @@
 
 Python version of the OpenPTV library - this is *a work in progress*
 
+## What This Repo Provides
+
+`openptv-python` keeps the Python API as the main interface and combines three
+execution modes behind that API:
+
+- Pure Python: the reference implementation and the easiest path for reading,
+  debugging, and extending the code.
+- Python + Numba: several hot kernels are JIT-compiled automatically on first
+  use, so the Python implementation still benefits from acceleration.
+- Native `optv` bindings: selected operations reuse the native OpenPTV
+  implementation when the `optv` package is available.
+
+At the moment, automatic native delegation is implemented for image
+preprocessing and full-frame target recognition. The rest of the library keeps
+the same Python API and remains usable even when those native paths are not in
+use.
+
 ## How this is started
 
 This work started from the https://github.com/OpenPTV/openptv/tree/pure_python branch. It's a long-standing idea to convert all the C code to Python and now it's possible with ChatGPT to save
@@ -10,13 +27,123 @@ a lot of typing time.
 This repo is created using a *cookiecutter* and the rest of the readme describes the way to work with
 this structure
 
-### Quick Start
+## Supported Python Versions
+
+The project currently supports Python `>=3.12,<3.14`.
+
+## Installation
+
+### Recommended: uv
+
+Create the environment and install all project dependencies:
+
+```bash
+uv venv
+source .venv/bin/activate
+uv sync
+```
+
+This is the simplest way to get the current default stack, including NumPy,
+SciPy, Numba, and the `optv` native bindings when they are available for your
+platform and Python version.
+
+### Alternative: conda + pip
+
+```bash
+conda create -n openptv-python -c conda-forge python=3.12
+conda activate openptv-python
+pip install -e .
+```
+
+### What gets installed
+
+- `numba` is part of the default dependency set and accelerates selected Python
+  kernels automatically after the first call.
+- `optv` is part of the default dependency set and enables native interop on
+  supported platforms.
+- The public API stays the same regardless of which backend is active.
+
+## Backend Behavior
+
+### Pure Python backend
+
+This is the base implementation for the whole library. It is always the source
+of truth for the Python API and remains the fallback behavior for code paths
+that are not delegated to `optv`.
+
+### Python + Numba backend
+
+Numba accelerates selected computational kernels inside the Python
+implementation. This is automatic; there is no separate API to enable it.
+Expect the first call to a JIT-compiled function to be slower due to
+compilation, with later calls running faster.
+
+### Native `optv` backend
+
+When `optv` imports successfully, `openptv-python` automatically reuses native
+implementations for:
+
+- image preprocessing
+- full-frame target recognition / segmentation
+
+These native paths are validated against the Python implementation by parity
+tests, so results stay backend-independent.
+
+### Backend Capability Table
+
+| Operation | Pure Python | Python + Numba | Native `optv` |
+| --- | --- | --- | --- |
+| Image preprocessing | Yes | Yes | Yes, automatic delegation |
+| Target recognition / segmentation | Yes | Yes | Yes, automatic delegation |
+| Point reconstruction | Yes | Partial internal kernels | Not used by default |
+| Correspondence search / stereo matching | Yes | Partial internal kernels | Not used by default |
+| Tracking | Yes | Partial internal kernels | Not used by default |
+| Sequence parameter I/O | Yes | No | Available in native bindings |
+
+`Not used by default` means the native path exists in benchmarks or conversion
+helpers, but the regular `openptv-python` runtime path still uses the Python
+implementation unless that operation is explicitly integrated later.
+
+## Getting Started
+
+### 1. Install the project
+
+Use one of the installation methods above.
+
+### 2. Verify imports
+
+```bash
+uv run python - <<'PY'
+import openptv_python
+import numba
+import optv
+
+print("openptv_python ok")
+print("numba ok", numba.__version__)
+print("optv ok", optv.__version__)
+PY
+```
+
+### 3. Start using the Python API
 
 ```python
 >>> import openptv_python
 
 ```
 
+### 4. Run the test suite
+
+```bash
+uv run make
+```
+
+Stress and performance tests are part of the default suite now. If you need a
+faster validation pass locally, you can skip them explicitly:
+
+```bash
+OPENPTV_SKIP_STRESS_BENCHMARKS=1 uv run make
+```
+
 ### Workflow for developers/contributors
 
 For the best experience create a new conda environment (e.g. DEVELOP) with Python 3.12:
diff --git a/docs/index.md b/docs/index.md
index 37ea85c..d414197 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -2,6 +2,15 @@
 
 Python version of the OpenPTV library.
 
+Start with the README for installation and backend selection details. The short
+version is:
+
+- install with `uv sync` on Python 3.12 or 3.13
+- use the same Python API regardless of backend
+- get pure Python behavior everywhere, Numba acceleration in selected kernels,
+  and automatic native `optv` delegation for preprocessing and full-frame
+  target recognition when available
+
 ```{toctree}
 :caption: 'Contents:'
 :maxdepth: 2
diff --git a/tests/test_native_stress_performance.py b/tests/test_native_stress_performance.py
index a97c24b..ad7be44 100644
--- a/tests/test_native_stress_performance.py
+++ b/tests/test_native_stress_performance.py
@@ -94,7 +94,7 @@ def _env_flag_enabled(name: str) -> bool:
     return os.getenv(name, "").strip().lower() in {"1", "true", "yes", "on"}
 
 
-RUN_STRESS_BENCHMARKS = _env_flag_enabled("OPENPTV_RUN_STRESS_BENCHMARKS")
+RUN_STRESS_BENCHMARKS = not _env_flag_enabled("OPENPTV_SKIP_STRESS_BENCHMARKS")
 
 
 def _benchmark(function, *, warmups: int = 1, runs: int = 3):
@@ -441,7 +441,7 @@ def _run_native_tracking_fixture() -> dict[str, tuple[int, str]]:
 
 @unittest.skipUnless(
     RUN_STRESS_BENCHMARKS,
-    "set OPENPTV_RUN_STRESS_BENCHMARKS=1 to run stress benchmarks",
+    "set OPENPTV_SKIP_STRESS_BENCHMARKS=1 to skip stress benchmarks",
 )
 class TestNativeStressPerformance(unittest.TestCase):
     """Stress tests comparing native and non-native runtime paths."""

From d63ed211bfafc9c9f034f362c60d789a49f49ce5 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 22:52:43 +0200
Subject: [PATCH 17/27] added numba to stereo matching

---
 README.html                             |  22 ++--
 openptv_python/orientation.py           | 111 +++++++++++++++++--
 tests/test_native_stress_performance.py |  48 ++++++--
 tests/test_point_positions.py           | 139 ++++++++++++++++++++++++
 4 files changed, 292 insertions(+), 28 deletions(-)

diff --git a/README.html b/README.html
index 8096b12..7dbf403 100644
--- a/README.html
+++ b/README.html
@@ -17,7 +17,7 @@
 ul.task-list{list-style: none;}
 ul.task-list li input[type="checkbox"] {
   width: 0.8em;
-  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
+  margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */
   vertical-align: middle;
 }
 /* CSS for syntax highlighting */
@@ -283,7 +283,7 @@ <h3 class="anchored" data-anchor-id="license">License</h3>
     anchorJS.add('.anchored');
     const isCodeAnnotation = (el) => {
       for (const clz of el.classList) {
-        if (clz.startsWith('code-annotation-')) {                     
+        if (clz.startsWith('code-annotation-')) {
           return true;
         }
       }
@@ -303,11 +303,11 @@ <h3 class="anchored" data-anchor-id="license">License</h3>
         button.setAttribute("data-bs-toggle", "tooltip");
         button.setAttribute("data-bs-placement", "left");
         button.setAttribute("data-bs-title", "Copied!");
-        tooltip = new bootstrap.Tooltip(button, 
-          { trigger: "manual", 
+        tooltip = new bootstrap.Tooltip(button,
+          { trigger: "manual",
             customClass: "code-copy-button-tooltip",
             offset: [0, -8]});
-        tooltip.show();    
+        tooltip.show();
       }
       setTimeout(function() {
         if (tooltip) {
@@ -383,7 +383,7 @@ <h3 class="anchored" data-anchor-id="license">License</h3>
       if (onUntriggerFn) {
         config.onUntrigger = onUntriggerFn;
       }
-      window.tippy(el, config); 
+      window.tippy(el, config);
     }
     const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
     for (var i=0; i<noterefs.length; i++) {
@@ -458,7 +458,7 @@ <h3 class="anchored" data-anchor-id="license">License</h3>
       tippyHover(xref, undefined, function(instance) {
         instance.disable();
         let url = xref.getAttribute('href');
-        let hash = undefined; 
+        let hash = undefined;
         if (url.startsWith('#')) {
           hash = url;
         } else {
@@ -486,7 +486,7 @@ <h3 class="anchored" data-anchor-id="license">License</h3>
               if (note !== null) {
                 const html = processXRef(id, note);
                 instance.setContent(html);
-              } 
+              }
             }).finally(() => {
               instance.enable();
               instance.show();
@@ -510,7 +510,7 @@ <h3 class="anchored" data-anchor-id="license">License</h3>
               }
               const html = processXRef(null, note);
               instance.setContent(html);
-            } 
+            }
           }).finally(() => {
             instance.enable();
             instance.show();
@@ -550,7 +550,7 @@ <h3 class="anchored" data-anchor-id="license">License</h3>
               height = bottom - top;
             }
             if (top !== null && height !== null && parent !== null) {
-              // cook up a div (if necessary) and position it 
+              // cook up a div (if necessary) and position it
               let div = window.document.getElementById("code-annotation-line-highlight");
               if (div === null) {
                 div = window.document.createElement("div");
@@ -676,4 +676,4 @@ <h3 class="anchored" data-anchor-id="license">License</h3>
 
 
 
-</body></html>
\ No newline at end of file
+</body></html>
diff --git a/openptv_python/orientation.py b/openptv_python/orientation.py
index e996505..9e8fc91 100644
--- a/openptv_python/orientation.py
+++ b/openptv_python/orientation.py
@@ -4,7 +4,7 @@
 
 import numpy as np
 import scipy
-from numba import njit
+from numba import njit, prange
 
 from openptv_python.constants import COORD_UNUSED
 
@@ -15,7 +15,7 @@
 
 # from .lsqadj import ata, atl, matinv, matmul
 from .parameters import ControlPar, MultimediaPar, OrientPar, VolumePar
-from .ray_tracing import ray_tracing
+from .ray_tracing import fast_ray_tracing, ray_tracing
 from .sortgrid import sortgrid
 from .tracking_frame_buf import Target
 from .trafo import (
@@ -55,6 +55,79 @@ def skew_midpoint(
     return float(scale), res
 
 
+@njit(cache=True, fastmath=True, nogil=True, parallel=True)
+def _multi_cam_point_positions_numba(
+    targets: np.ndarray,
+    distortion_matrices: np.ndarray,
+    primary_points: np.ndarray,
+    glass_vectors: np.ndarray,
+    ccs: np.ndarray,
+    distance_param: float,
+    refractive_index_medium1: float,
+    refractive_index_medium2: float,
+    refractive_index_medium3: float,
+    coord_unused: float,
+) -> Tuple[np.ndarray, np.ndarray]:
+    """Calculate multi-camera point positions using compiled per-point loops."""
+    num_targets = targets.shape[0]
+    num_cams = targets.shape[1]
+    res = np.empty((num_targets, 3), dtype=np.float64)
+    rcm = np.empty(num_targets, dtype=np.float64)
+
+    for pt in prange(num_targets):
+        vertices = np.zeros((num_cams, 3), dtype=np.float64)
+        directs = np.zeros((num_cams, 3), dtype=np.float64)
+        point_tot = np.zeros(3, dtype=np.float64)
+        num_used_pairs = 0
+        dtot = 0.0
+
+        for cam in range(num_cams):
+            if targets[pt, cam, 0] == coord_unused:
+                continue
+
+            camera = np.empty(3, dtype=np.float64)
+            camera[0] = targets[pt, cam, 0]
+            camera[1] = targets[pt, cam, 1]
+            camera[2] = -ccs[cam]
+            vertices[cam], directs[cam] = fast_ray_tracing(
+                camera,
+                distortion_matrices[cam],
+                primary_points[cam],
+                glass_vectors[cam],
+                distance_param,
+                refractive_index_medium1,
+                refractive_index_medium2,
+                refractive_index_medium3,
+            )
+
+        for cam in range(num_cams):
+            if targets[pt, cam, 0] == coord_unused:
+                continue
+
+            for pair in range(cam + 1, num_cams):
+                if targets[pt, pair, 0] == coord_unused:
+                    continue
+
+                num_used_pairs += 1
+                tmp, point = skew_midpoint(
+                    vertices[cam],
+                    directs[cam],
+                    vertices[pair],
+                    directs[pair],
+                )
+                dtot += tmp
+                point_tot += point
+
+        if num_used_pairs == 0:
+            rcm[pt] = np.nan
+            res[pt, :] = np.nan
+        else:
+            rcm[pt] = dtot / num_used_pairs
+            res[pt, :] = point_tot / num_used_pairs
+
+    return res, rcm
+
+
 def point_position(
     targets: np.ndarray,
     num_cams: int,
@@ -1060,15 +1133,33 @@ def multi_cam_point_positions(
 
     # So we can address targets.data directly instead of get_ptr stuff:
 
-    num_targets = targets.shape[0]
-    num_cams = targets.shape[1]
-    res = np.empty((num_targets, 3))
-    rcm = np.empty(num_targets)
-
-    for pt in range(num_targets):
-        rcm[pt], res[pt] = point_position(targets[pt], num_cams, mm_par, cals)
+    distortion_matrices = np.ascontiguousarray(
+        np.stack([cal.ext_par.dm for cal in cals]).astype(np.float64)
+    )
+    primary_points = np.ascontiguousarray(
+        np.stack(
+            [np.array([cal.ext_par.x0, cal.ext_par.y0, cal.ext_par.z0]) for cal in cals]
+        ).astype(np.float64)
+    )
+    glass_vectors = np.ascontiguousarray(
+        np.stack([cal.glass_par for cal in cals]).astype(np.float64)
+    )
+    ccs = np.ascontiguousarray(
+        np.array([cal.int_par.cc for cal in cals], dtype=np.float64)
+    )
 
-    return res, rcm
+    return _multi_cam_point_positions_numba(
+        np.ascontiguousarray(targets, dtype=np.float64),
+        distortion_matrices,
+        primary_points,
+        glass_vectors,
+        ccs,
+        float(mm_par.d[0]),
+        float(mm_par.n1),
+        float(mm_par.n2[0]),
+        float(mm_par.n3),
+        float(COORD_UNUSED),
+    )
 
 
 def _clone_calibration(cal: Calibration) -> Calibration:
diff --git a/tests/test_native_stress_performance.py b/tests/test_native_stress_performance.py
index ad7be44..cceca9a 100644
--- a/tests/test_native_stress_performance.py
+++ b/tests/test_native_stress_performance.py
@@ -195,6 +195,27 @@ def _build_reconstruction_stress_case(
     return points, targets, cpar, calibs
 
 
+def _python_point_positions_reference(
+    targets: np.ndarray,
+    mm_par,
+    cals: list[Calibration],
+) -> tuple[np.ndarray, np.ndarray]:
+    """Reconstruct points with the original Python per-target loop."""
+    num_targets = targets.shape[0]
+    points = np.empty((num_targets, 3), dtype=np.float64)
+    rcm = np.empty(num_targets, dtype=np.float64)
+
+    for pt in range(num_targets):
+        rcm[pt], points[pt] = orientation.point_position(
+            targets[pt],
+            len(cals),
+            mm_par,
+            cals,
+        )
+
+    return points, rcm
+
+
 def _build_stereomatching_stress_case(
     grid_width: int = 8,
     grid_height: int = 8,
@@ -645,38 +666,51 @@ def native_path():
         "optv native point_positions is not available",
     )
     def test_point_reconstruction_stress_timing(self):
-        """Compare native multi-camera reconstruction timing against Python."""
+        """Compare compiled Python, legacy Python, and native reconstruction."""
         expected_points, targets, cpar, calibs = _build_reconstruction_stress_case()
         native_cpar = to_native_control_par(cpar)
         native_cals = [to_native_calibration(cal) for cal in calibs]
         native_vpar = NativeVolumeParams()
         python_vpar = VolumePar()
 
-        def python_path():
+        def compiled_python_path():
             return orientation.point_positions(targets, cpar.mm, calibs, python_vpar)
 
+        def legacy_python_path():
+            return _python_point_positions_reference(targets, cpar.mm, calibs)
+
         def native_path():
             assert native_point_positions is not None
             return native_point_positions(
                 targets, native_cpar, native_cals, native_vpar
             )
 
-        python_result, python_timings = _benchmark(python_path)
+        compiled_python_result, compiled_python_timings = _benchmark(compiled_python_path)
+        legacy_python_result, legacy_python_timings = _benchmark(legacy_python_path)
         native_result, native_timings = _benchmark(native_path)
-        python_points, python_rcm = python_result
+        python_points, python_rcm = compiled_python_result
+        legacy_points, legacy_rcm = legacy_python_result
         native_points, native_rcm = native_result
 
         np.testing.assert_allclose(python_points, expected_points, atol=1e-6)
+        np.testing.assert_allclose(legacy_points, expected_points, atol=1e-6)
         np.testing.assert_allclose(native_points, expected_points, atol=1e-6)
+        np.testing.assert_allclose(legacy_points, python_points, atol=1e-9)
+        np.testing.assert_allclose(legacy_rcm, python_rcm, atol=1e-9)
         np.testing.assert_allclose(native_points, python_points, atol=1e-9)
         np.testing.assert_allclose(native_rcm, python_rcm, atol=1e-9)
 
-        speedup = median(python_timings) / median(native_timings)
+        compiled_vs_legacy = median(legacy_python_timings) / median(
+            compiled_python_timings
+        )
+        compiled_vs_native = median(compiled_python_timings) / median(native_timings)
         print(
             "reconstruction stress benchmark: "
-            f"{_timing_summary('python', python_timings)}; "
+            f"{_timing_summary('compiled-python', compiled_python_timings)}; "
+            f"{_timing_summary('legacy-python', legacy_python_timings)}; "
             f"{_timing_summary('native', native_timings)}; "
-            f"speedup={speedup:.2f}x"
+            f"compiled-vs-legacy={compiled_vs_legacy:.2f}x; "
+            f"compiled-vs-native={compiled_vs_native:.2f}x"
         )
 
     @unittest.skipUnless(
diff --git a/tests/test_point_positions.py b/tests/test_point_positions.py
index a2cf0f6..a4eeef9 100644
--- a/tests/test_point_positions.py
+++ b/tests/test_point_positions.py
@@ -3,11 +3,15 @@
 
 import numpy as np
 
+from openptv_python._native_compat import HAS_OPTV
+from openptv_python._native_convert import to_native_calibration, to_native_control_par
 from openptv_python.calibration import (
     Calibration,
 )
+from openptv_python.constants import COORD_UNUSED
 from openptv_python.imgcoord import flat_image_coordinates, image_coordinates
 from openptv_python.orientation import (
+    point_position,
     match_detection_to_ref,
     point_positions,
     weighted_dumbbell_precision,
@@ -16,6 +20,28 @@
 from openptv_python.tracking_frame_buf import Target
 from openptv_python.trafo import arr_metric_to_pixel
 
+try:
+    from optv.orientation import point_positions as native_point_positions
+    from optv.parameters import VolumeParams as NativeVolumeParams
+
+    HAS_NATIVE_RECONSTRUCTION = True
+except ImportError:
+    native_point_positions = None
+    NativeVolumeParams = None
+    HAS_NATIVE_RECONSTRUCTION = False
+
+
+def _python_multi_cam_point_positions_reference(targets, mm_par, cals):
+    """Reconstruct points with the original per-target Python loop."""
+    num_targets = targets.shape[0]
+    points = np.empty((num_targets, 3), dtype=np.float64)
+    rcm = np.empty(num_targets, dtype=np.float64)
+
+    for pt in range(num_targets):
+        rcm[pt], points[pt] = point_position(targets[pt], len(cals), mm_par, cals)
+
+    return points, rcm
+
 
 class TestOrientation(unittest.TestCase):
     """Test the orientation module."""
@@ -165,6 +191,119 @@ def test_point_positions(self):
         if np.any(np.linalg.norm(points - skew_dist_jigged[0], axis=1) > 0.1):
             self.fail("Rays converge on wrong position after jigging.")
 
+    def test_multi_camera_point_positions_matches_python_reference_loop(self):
+        """Compiled multi-camera reconstruction matches the original Python loop."""
+        mult_params = MultimediaPar()
+        mult_params.set_n1(1.0)
+        mult_params.set_layers([1.0], [1.0])
+        mult_params.set_n3(1.0)
+
+        points = np.array(
+            [
+                [17.0, 42.0, 0.0],
+                [12.5, 35.0, -4.0],
+                [-8.0, 50.0, 7.5],
+            ],
+            dtype=np.float64,
+        )
+
+        add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
+        calibs = [
+            Calibration().from_file(
+                ori_file=Path(
+                    f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"
+                ),
+                add_file=add_file,
+            )
+            for cam_num in range(1, 5)
+        ]
+
+        projections = [image_coordinates(points, cal, mult_params) for cal in calibs]
+        targets = np.asarray(projections, dtype=np.float64).transpose(1, 0, 2)
+
+        partial_targets = targets.copy()
+        partial_targets[0, 3, :] = COORD_UNUSED
+        partial_targets[1, 1, :] = COORD_UNUSED
+        partial_targets[2, 0, :] = COORD_UNUSED
+        partial_targets[2, 2, :] = COORD_UNUSED
+
+        compiled_points, compiled_rcm = point_positions(
+            partial_targets,
+            mult_params,
+            calibs,
+            self.vpar,
+        )
+        reference_points, reference_rcm = _python_multi_cam_point_positions_reference(
+            partial_targets,
+            mult_params,
+            calibs,
+        )
+
+        np.testing.assert_allclose(compiled_points, reference_points, atol=1e-9)
+        np.testing.assert_allclose(compiled_rcm, reference_rcm, atol=1e-9)
+        np.testing.assert_allclose(compiled_points, points, atol=1e-6)
+
+    @unittest.skipUnless(
+        HAS_OPTV and HAS_NATIVE_RECONSTRUCTION,
+        "optv native point_positions is not available",
+    )
+    def test_multi_camera_point_positions_matches_native_backend(self):
+        """Compiled multi-camera reconstruction matches optv output."""
+        mult_params = MultimediaPar()
+        mult_params.set_n1(1.0)
+        mult_params.set_layers([1.0], [1.0])
+        mult_params.set_n3(1.0)
+
+        points = np.array(
+            [
+                [17.0, 42.0, 0.0],
+                [12.5, 35.0, -4.0],
+                [-8.0, 50.0, 7.5],
+            ],
+            dtype=np.float64,
+        )
+
+        cpar = ControlPar(4).from_file(self.control_file_name)
+        cpar.mm.set_n1(1.0)
+        cpar.mm.set_layers([1.0], [1.0])
+        cpar.mm.set_n3(1.0)
+
+        add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
+        calibs = [
+            Calibration().from_file(
+                ori_file=Path(
+                    f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"
+                ),
+                add_file=add_file,
+            )
+            for cam_num in range(1, 5)
+        ]
+
+        projections = [image_coordinates(points, cal, cpar.mm) for cal in calibs]
+        targets = np.asarray(projections, dtype=np.float64).transpose(1, 0, 2)
+
+        python_points, python_rcm = point_positions(targets, cpar.mm, calibs, self.vpar)
+        reference_points, reference_rcm = _python_multi_cam_point_positions_reference(
+            targets,
+            cpar.mm,
+            calibs,
+        )
+
+        assert native_point_positions is not None
+        assert NativeVolumeParams is not None
+        native_points, native_rcm = native_point_positions(
+            targets,
+            to_native_control_par(cpar),
+            [to_native_calibration(cal) for cal in calibs],
+            NativeVolumeParams(),
+        )
+
+        np.testing.assert_allclose(python_points, reference_points, atol=1e-9)
+        np.testing.assert_allclose(python_rcm, reference_rcm, atol=1e-9)
+        np.testing.assert_allclose(native_points, python_points, atol=1e-9)
+        np.testing.assert_allclose(native_rcm, python_rcm, atol=1e-9)
+        np.testing.assert_allclose(python_points, points, atol=1e-6)
+
     def test_single_camera_point_positions(self):
         """Point positions for a single camera case."""
         num_cams = 1

From a3bfbd121b4d18e023acb193c96fa96990bb2b41 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 7 Mar 2026 23:46:18 +0200
Subject: [PATCH 18/27] improved mutlimedia

---
 openptv_python/multimed.py              |  13 +-
 openptv_python/parameters.py            |   2 +-
 tests/test_multimedia_n_lay.py          | 242 +++++++++++++++++++++++-
 tests/test_native_stress_performance.py |  71 ++++++-
 tests/test_point_positions.py           |   2 +-
 5 files changed, 320 insertions(+), 10 deletions(-)

diff --git a/openptv_python/multimed.py b/openptv_python/multimed.py
index d2a32c2..236f534 100644
--- a/openptv_python/multimed.py
+++ b/openptv_python/multimed.py
@@ -80,17 +80,18 @@ def fast_multimed_r_nlay(
         zdiff = 1.0
     it = 0
     rdiff = 0.1  # Initialize to enter the loop
+    beta2 = np.zeros(nlay, dtype=np.float64)
 
     while abs(rdiff) > 0.001 and it < n_iter:
         beta1 = np.arctan(rq / zdiff)
-        beta2 = np.arcsin(np.sin(beta1) * n1 / n2[0])
+        sin_beta1 = np.sin(beta1)
+        for layer in range(nlay):
+            beta2[layer] = np.arcsin(sin_beta1 * n1 / n2[layer])
         beta3 = np.arcsin(np.sin(beta1) * n1 / n3)
 
-        rbeta = (
-            (z0 - d[0]) * np.tan(beta1)
-            - zout * np.tan(beta3)
-            + np.sum(d * np.tan(beta2))
-        )
+        rbeta = (z0 - d[0]) * np.tan(beta1) - zout * np.tan(beta3)
+        for layer in range(nlay):
+            rbeta += d[layer] * np.tan(beta2[layer])
 
         rdiff = r - rbeta
         rq += rdiff
diff --git a/openptv_python/parameters.py b/openptv_python/parameters.py
index a5ea3d2..d9062e6 100644
--- a/openptv_python/parameters.py
+++ b/openptv_python/parameters.py
@@ -105,7 +105,7 @@ def set_layers(self, refr_index: list[float], thickness: list[float]):
         else:
             self.n2 = refr_index
             self.d = thickness
-            # self.nlay = len(refr_index)
+            self.nlay = len(refr_index)
 
     def __str__(self) -> str:
         return f"nlay = {self.nlay}, n1 = {self.n1}, n2 = {self.n2}, d = {self.d}, n3 = {self.n3}"
diff --git a/tests/test_multimedia_n_lay.py b/tests/test_multimedia_n_lay.py
index a4f14e4..aa7be0b 100644
--- a/tests/test_multimedia_n_lay.py
+++ b/tests/test_multimedia_n_lay.py
@@ -3,9 +3,128 @@
 
 import numpy as np
 
+from openptv_python._native_compat import HAS_OPTV
+from openptv_python._native_convert import to_native_calibration, to_native_control_par
 from openptv_python.calibration import read_calibration
+from openptv_python.epi import epipolar_curve
+from openptv_python.imgcoord import image_coordinates
 from openptv_python.multimed import init_mmlut, multimed_nlay, multimed_r_nlay
-from openptv_python.parameters import read_control_par, read_volume_par
+from openptv_python.orientation import point_position, point_positions
+from openptv_python.parameters import (
+    ControlPar,
+    MultimediaPar,
+    VolumePar,
+    read_control_par,
+    read_volume_par,
+)
+from openptv_python.trafo import metric_to_pixel
+
+try:
+    from optv.epipolar import epipolar_curve as native_epipolar_curve
+    from optv.orientation import (
+        multi_cam_point_positions as native_multi_cam_point_positions,
+    )
+    from optv.parameters import VolumeParams as NativeVolumeParams
+
+    HAS_NATIVE_MULTIMEDIA_PARITY = True
+except ImportError:
+    native_epipolar_curve = None
+    native_multi_cam_point_positions = None
+    NativeVolumeParams = None
+    HAS_NATIVE_MULTIMEDIA_PARITY = False
+
+
+def _multimed_r_nlay_reference(cal, mm, pos):
+    """Match the native C multilayer loop in a Python reference implementation."""
+    if mm.n1 == 1 and mm.nlay == 1 and mm.n2[0] == 1 and mm.n3 == 1:
+        return 1.0
+
+    x, y, z = pos
+    zout = z + sum(mm.d[1 : mm.nlay])
+    r = float(np.linalg.norm(np.array([x - cal.ext_par.x0, y - cal.ext_par.y0])))
+    rq = r
+    rdiff = 0.1
+    it = 0
+
+    while abs(rdiff) > 0.001 and it < 40:
+        beta1 = np.arctan(rq / (cal.ext_par.z0 - z))
+        beta2 = [np.arcsin(np.sin(beta1) * mm.n1 / mm.n2[i]) for i in range(mm.nlay)]
+        beta3 = np.arcsin(np.sin(beta1) * mm.n1 / mm.n3)
+
+        rbeta = (cal.ext_par.z0 - mm.d[0]) * np.tan(beta1) - zout * np.tan(beta3)
+        for layer in range(mm.nlay):
+            rbeta += mm.d[layer] * np.tan(beta2[layer])
+
+        rdiff = r - rbeta
+        rq += rdiff
+        it += 1
+
+    return 1.0 if r == 0 else float(rq / r)
+
+
+def _python_multi_cam_point_positions_reference(targets, mm_par, cals):
+    """Reconstruct points with the original Python per-point loop."""
+    num_targets = targets.shape[0]
+    points = np.empty((num_targets, 3), dtype=np.float64)
+    rcm = np.empty(num_targets, dtype=np.float64)
+
+    for pt in range(num_targets):
+        rcm[pt], points[pt] = point_position(targets[pt], len(cals), mm_par, cals)
+
+    return points, rcm
+
+
+def _to_native_volume_par(vpar: VolumePar):
+    """Convert Python volume parameters into native VolumeParams."""
+    if NativeVolumeParams is None:
+        raise RuntimeError("optv VolumeParams is not available")
+
+    native_vpar = NativeVolumeParams()
+    native_vpar.set_X_lay(list(vpar.x_lay))
+    native_vpar.set_Zmin_lay(list(vpar.z_min_lay))
+    native_vpar.set_Zmax_lay(list(vpar.z_max_lay))
+    native_vpar.set_cn(vpar.cn)
+    native_vpar.set_cnx(vpar.cnx)
+    native_vpar.set_cny(vpar.cny)
+    native_vpar.set_csumg(vpar.csumg)
+    native_vpar.set_eps0(vpar.eps0)
+    native_vpar.set_corrmin(vpar.corrmin)
+    return native_vpar
+
+
+def _build_multilayer_case():
+    """Build a deterministic multi-camera multilayer reconstruction fixture."""
+    cpar = ControlPar(4).from_file(
+        Path("tests/testing_folder/control_parameters/control.par")
+    )
+    cpar.mm.set_n1(1.0)
+    cpar.mm.set_layers([1.49, 1.10], [5.0, 10.0])
+    cpar.mm.set_n3(1.33)
+
+    vpar = read_volume_par(Path("tests/testing_folder/corresp/criteria.par"))
+    add_file = Path("tests/testing_folder/calibration/cam1.tif.addpar")
+    calibs = [
+        read_calibration(
+            Path(f"tests/testing_folder/calibration/sym_cam{cam_num}.tif.ori"),
+            add_file,
+        )
+        for cam_num in range(1, 5)
+    ]
+
+    points = np.array(
+        [
+            [17.0, 42.0, 0.0],
+            [8.0, 36.0, -4.0],
+            [-6.0, 55.0, 7.0],
+        ],
+        dtype=np.float64,
+    )
+    targets = np.asarray(
+        [image_coordinates(points, cal, cpar.mm) for cal in calibs],
+        dtype=np.float64,
+    ).transpose(1, 0, 2)
+
+    return cpar, vpar, calibs, points, targets
 
 
 class TestMultimedRnlay(unittest.TestCase):
@@ -74,6 +193,127 @@ def test_multimed_r_nlay_2(self):
         self.assertAlmostEqual(Xq, correct_Xq, delta=1e-6)
         self.assertAlmostEqual(Yq, correct_Yq, delta=1e-6)
 
+    def test_multimed_r_nlay_matches_native_formula_for_multiple_layers(self):
+        """Multi-layer radial shift matches the native per-layer formulation."""
+        mm = MultimediaPar(
+            nlay=2,
+            n1=1.0,
+            n2=[1.49, 1.10],
+            d=[5.0, 10.0],
+            n3=1.33,
+        )
+        pos = np.array([10.0, 15.0, 1.23], dtype=np.float64)
+
+        observed = multimed_r_nlay(self.cal, mm, pos)
+        expected = _multimed_r_nlay_reference(self.cal, mm, pos)
+
+        self.assertAlmostEqual(observed, expected, delta=1e-10)
+
+    def test_multimed_r_nlay_matches_reference_across_multilayer_cases(self):
+        """Multi-layer radial shift stays aligned with the native loop across cases."""
+        cases = [
+            (
+                MultimediaPar(nlay=2, n1=1.0, n2=[1.49, 1.10], d=[5.0, 10.0], n3=1.33),
+                np.array([10.0, 15.0, 1.23], dtype=np.float64),
+            ),
+            (
+                MultimediaPar(nlay=2, n1=1.0, n2=[1.49, 1.20], d=[5.0, 3.0], n3=1.33),
+                np.array([1.23, 1.23, 1.23], dtype=np.float64),
+            ),
+            (
+                MultimediaPar(
+                    nlay=3,
+                    n1=1.0,
+                    n2=[1.49, 1.20, 1.05],
+                    d=[5.0, 3.0, 1.5],
+                    n3=1.33,
+                ),
+                np.array([12.0, -6.0, 2.5], dtype=np.float64),
+            ),
+        ]
+
+        for mm, pos in cases:
+            with self.subTest(mm=mm, pos=pos):
+                observed = multimed_r_nlay(self.cal, mm, pos)
+                expected = _multimed_r_nlay_reference(self.cal, mm, pos)
+                self.assertAlmostEqual(observed, expected, delta=1e-10)
+
+    @unittest.skipUnless(
+        HAS_OPTV and HAS_NATIVE_MULTIMEDIA_PARITY,
+        "optv native multimedia comparison hooks are not available",
+    )
+    def test_multilayer_reconstruction_matches_python_numba_and_native(self):
+        """Multi-layer reconstruction agrees across Python, Numba, and optv."""
+        cpar, vpar, calibs, points, targets = _build_multilayer_case()
+
+        python_points, python_rcm = _python_multi_cam_point_positions_reference(
+            targets,
+            cpar.mm,
+            calibs,
+        )
+        compiled_points, compiled_rcm = point_positions(targets, cpar.mm, calibs, vpar)
+
+        assert native_multi_cam_point_positions is not None
+        native_points, native_rcm = native_multi_cam_point_positions(
+            targets,
+            to_native_control_par(cpar),
+            [to_native_calibration(cal) for cal in calibs],
+        )
+
+        np.testing.assert_allclose(compiled_points, python_points, atol=1e-9)
+        np.testing.assert_allclose(compiled_rcm, python_rcm, atol=1e-9)
+        np.testing.assert_allclose(native_points, compiled_points, atol=1e-9)
+        np.testing.assert_allclose(native_rcm, compiled_rcm, atol=1e-9)
+
+        reprojected = np.asarray(
+            [image_coordinates(compiled_points, cal, cpar.mm) for cal in calibs],
+            dtype=np.float64,
+        ).transpose(1, 0, 2)
+        self.assertLess(np.max(np.abs(reprojected - targets)), 0.25)
+        self.assertTrue(np.all(np.isfinite(compiled_rcm)))
+
+    @unittest.skipUnless(
+        HAS_OPTV and HAS_NATIVE_MULTIMEDIA_PARITY,
+        "optv native multimedia comparison hooks are not available",
+    )
+    def test_multilayer_epipolar_curve_matches_native(self):
+        """Multi-layer epipolar geometry matches the native binding output."""
+        cpar, vpar, calibs, points, _targets = _build_multilayer_case()
+        origin_projection = image_coordinates(points[:1], calibs[0], cpar.mm)[0]
+        image_point = np.array(
+            metric_to_pixel(origin_projection[0], origin_projection[1], cpar)
+        )
+
+        python_line = epipolar_curve(
+            image_point,
+            calibs[0],
+            calibs[2],
+            9,
+            cpar,
+            vpar,
+        )
+
+        assert native_epipolar_curve is not None
+        native_line = native_epipolar_curve(
+            image_point,
+            to_native_calibration(calibs[0]),
+            to_native_calibration(calibs[2]),
+            9,
+            to_native_control_par(cpar),
+            _to_native_volume_par(vpar),
+        )
+
+        np.testing.assert_allclose(native_line, python_line, atol=1e-6)
+
+    def test_set_layers_updates_multimedia_layer_count(self):
+        """set_layers keeps nlay in sync with the provided layer arrays."""
+        mm = MultimediaPar()
+        mm.set_layers([1.49, 1.10], [5.0, 10.0])
+
+        self.assertEqual(mm.nlay, 2)
+        self.assertEqual(mm.n2, [1.49, 1.10])
+        self.assertEqual(mm.d, [5.0, 10.0])
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_native_stress_performance.py b/tests/test_native_stress_performance.py
index cceca9a..3b7a6c7 100644
--- a/tests/test_native_stress_performance.py
+++ b/tests/test_native_stress_performance.py
@@ -45,11 +45,15 @@
 from openptv_python.trafo import dist_to_flat, metric_to_pixel, pixel_to_metric
 
 try:
+    from optv.orientation import (
+        multi_cam_point_positions as native_multi_cam_point_positions,
+    )
     from optv.orientation import point_positions as native_point_positions
     from optv.parameters import VolumeParams as NativeVolumeParams
 
     HAS_NATIVE_RECONSTRUCTION = True
 except ImportError:
+    native_multi_cam_point_positions = None
     native_point_positions = None
     NativeVolumeParams = None
     HAS_NATIVE_RECONSTRUCTION = False
@@ -195,6 +199,20 @@ def _build_reconstruction_stress_case(
     return points, targets, cpar, calibs
 
 
+def _build_multilayer_reconstruction_stress_case(
+    num_points: int = 4096,
+) -> tuple[np.ndarray, np.ndarray, ControlPar, list[Calibration]]:
+    """Build a deterministic multi-layer multi-camera reconstruction workload."""
+    points, _targets, cpar, calibs = _build_reconstruction_stress_case(num_points)
+    cpar.mm.set_n1(1.0)
+    cpar.mm.set_layers([1.49, 1.10], [5.0, 10.0])
+    cpar.mm.set_n3(1.33)
+
+    projections = [image_coordinates(points, cal, cpar.mm) for cal in calibs]
+    targets = np.asarray(projections, dtype=np.float64).transpose(1, 0, 2)
+    return points, targets, cpar, calibs
+
+
 def _python_point_positions_reference(
     targets: np.ndarray,
     mm_par,
@@ -685,7 +703,9 @@ def native_path():
                 targets, native_cpar, native_cals, native_vpar
             )
 
-        compiled_python_result, compiled_python_timings = _benchmark(compiled_python_path)
+        compiled_python_result, compiled_python_timings = _benchmark(
+            compiled_python_path
+        )
         legacy_python_result, legacy_python_timings = _benchmark(legacy_python_path)
         native_result, native_timings = _benchmark(native_path)
         python_points, python_rcm = compiled_python_result
@@ -713,6 +733,55 @@ def native_path():
             f"compiled-vs-native={compiled_vs_native:.2f}x"
         )
 
+    @unittest.skipUnless(
+        HAS_OPTV and HAS_NATIVE_RECONSTRUCTION,
+        "optv native point_positions is not available",
+    )
+    def test_multilayer_point_reconstruction_stress_timing(self):
+        """Compare compiled Python, legacy Python, and native multilayer reconstruction."""
+        _expected_points, targets, cpar, calibs = (
+            _build_multilayer_reconstruction_stress_case()
+        )
+        native_cpar = to_native_control_par(cpar)
+        native_cals = [to_native_calibration(cal) for cal in calibs]
+
+        def compiled_python_path():
+            return orientation.multi_cam_point_positions(targets, cpar.mm, calibs)
+
+        def legacy_python_path():
+            return _python_point_positions_reference(targets, cpar.mm, calibs)
+
+        def native_path():
+            assert native_multi_cam_point_positions is not None
+            return native_multi_cam_point_positions(targets, native_cpar, native_cals)
+
+        compiled_python_result, compiled_python_timings = _benchmark(
+            compiled_python_path
+        )
+        legacy_python_result, legacy_python_timings = _benchmark(legacy_python_path)
+        native_result, native_timings = _benchmark(native_path)
+        python_points, python_rcm = compiled_python_result
+        legacy_points, legacy_rcm = legacy_python_result
+        native_points, native_rcm = native_result
+
+        np.testing.assert_allclose(legacy_points, python_points, atol=1e-9)
+        np.testing.assert_allclose(legacy_rcm, python_rcm, atol=1e-9)
+        np.testing.assert_allclose(native_points, python_points, atol=1e-9)
+        np.testing.assert_allclose(native_rcm, python_rcm, atol=1e-9)
+
+        compiled_vs_legacy = median(legacy_python_timings) / median(
+            compiled_python_timings
+        )
+        compiled_vs_native = median(compiled_python_timings) / median(native_timings)
+        print(
+            "multilayer reconstruction stress benchmark: "
+            f"{_timing_summary('compiled-python', compiled_python_timings)}; "
+            f"{_timing_summary('legacy-python', legacy_python_timings)}; "
+            f"{_timing_summary('native', native_timings)}; "
+            f"compiled-vs-legacy={compiled_vs_legacy:.2f}x; "
+            f"compiled-vs-native={compiled_vs_native:.2f}x"
+        )
+
     @unittest.skipUnless(
         HAS_NATIVE_TRACKING,
         "optv native Tracker is not available",
diff --git a/tests/test_point_positions.py b/tests/test_point_positions.py
index a4eeef9..17a9ecb 100644
--- a/tests/test_point_positions.py
+++ b/tests/test_point_positions.py
@@ -11,8 +11,8 @@
 from openptv_python.constants import COORD_UNUSED
 from openptv_python.imgcoord import flat_image_coordinates, image_coordinates
 from openptv_python.orientation import (
-    point_position,
     match_detection_to_ref,
+    point_position,
     point_positions,
     weighted_dumbbell_precision,
 )

From 80c6bc21e0f43db26d6c30f802d08f1f331f1bb8 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sun, 8 Mar 2026 19:35:25 +0200
Subject: [PATCH 19/27] updated tracking test

---
 tests/test_tracking_ground_truth.py | 739 ++++++++++++++++++++++++++++
 1 file changed, 739 insertions(+)
 create mode 100644 tests/test_tracking_ground_truth.py

diff --git a/tests/test_tracking_ground_truth.py b/tests/test_tracking_ground_truth.py
new file mode 100644
index 0000000..afb7847
--- /dev/null
+++ b/tests/test_tracking_ground_truth.py
@@ -0,0 +1,739 @@
+import os
+import re
+import shutil
+import tempfile
+import unittest
+from contextlib import ExitStack, redirect_stdout
+from dataclasses import dataclass
+import io
+from pathlib import Path
+from unittest.mock import patch
+
+import numpy as np
+
+import openptv_python.track as track
+from openptv_python._native_compat import HAS_OPTV
+from openptv_python._native_convert import to_native_calibration, to_native_control_par
+from openptv_python.calibration import Calibration
+from openptv_python.constants import CORRES_NONE
+from openptv_python.imgcoord import image_coordinates
+from openptv_python.parameters import ControlPar, read_volume_par
+from openptv_python.track import track_forward_start, trackcorr_c_finish, trackcorr_c_loop
+from openptv_python.tracking_frame_buf import (
+    Frame,
+    Pathinfo,
+    Target,
+    read_path_frame,
+    write_path_frame,
+    write_targets,
+)
+from openptv_python.tracking_run import tr_new
+from openptv_python.trafo import metric_to_pixel
+
+try:
+    from optv.parameters import SequenceParams as NativeSequenceParams
+    from optv.parameters import TrackingParams as NativeTrackingParams
+    from optv.parameters import VolumeParams as NativeVolumeParams
+    from optv.tracker import Tracker as NativeTracker
+
+    HAS_NATIVE_TRACKING = True
+except ImportError:
+    NativeSequenceParams = None
+    NativeTrackingParams = None
+    NativeVolumeParams = None
+    NativeTracker = None
+    HAS_NATIVE_TRACKING = False
+
+
+FRAMES = tuple(range(10001, 10007))
+PERMISSIVE_BOUNDS = {
+    "dacc": 2.0,
+    "dangle": 100.0,
+    "dvxmin": -0.02,
+    "dvxmax": 0.02,
+    "dvymin": -0.02,
+    "dvymax": 0.02,
+    "dvzmin": -0.02,
+    "dvzmax": 0.02,
+}
+STRICT_BOUNDS = {
+    "dacc": 2.0,
+    "dangle": 100.0,
+    "dvxmin": -0.005,
+    "dvxmax": 0.005,
+    "dvymin": -0.005,
+    "dvymax": 0.005,
+    "dvzmin": -0.005,
+    "dvzmax": 0.005,
+}
+EXPECTED_PERMISSIVE_SNAPSHOT = {
+    10001: [(-1, 0, (2, 1, 1, -1), (0.0, 0.0, 0.0)), (-1, 1, (1, 0, 0, -1), (0.02, 0.01, 0.0))],
+    10002: [(0, 0, (2, 1, 1, -1), (0.01, 0.0, 0.0)), (1, 1, (1, 0, 0, -1), (0.03, 0.01, 0.0))],
+    10003: [(0, 0, (2, 1, 1, -1), (0.02, 0.0, 0.0)), (1, 1, (1, 0, 0, -1), (0.04, 0.01, 0.0))],
+    10004: [(0, 0, (2, 1, 1, -1), (0.03, 0.0, 0.0)), (1, 1, (1, 0, 0, -1), (0.05, 0.01, 0.0))],
+    10005: [(0, 0, (2, 1, 1, -1), (0.04, 0.0, 0.0)), (1, 1, (1, 0, 0, -1), (0.06, 0.01, 0.0))],
+    10006: [(0, -2, (2, 1, 1, -1), (0.05, 0.0, 0.0)), (1, -2, (1, 0, 0, -1), (0.07, 0.01, 0.0))],
+}
+EXPECTED_STRICT_SNAPSHOT = {
+    frame: [
+        (-1, -2, (2, 1, 1, -1), (round(0.01 * (frame - 10001), 5), 0.0, 0.0)),
+        (-1, -2, (1, 0, 0, -1), (round(0.02 + 0.01 * (frame - 10001), 5), 0.01, 0.0)),
+    ]
+    for frame in FRAMES
+}
+EXPECTED_PERMISSIVE_STEP_STATS = {frame: (2, 0) for frame in FRAMES[:-1]}
+EXPECTED_STRICT_STEP_STATS = {frame: (0, 2) for frame in FRAMES[:-1]}
+BAD_FILE_LOOKUP_PATTERN = re.compile(r"Can't open ascii file|\d{10}_targets")
+
+CONSTANT_DIAGONAL_TRACKS = {
+    0: [np.array([0.01 * index, 0.01 * index, 0.01 * index]) for index in range(len(FRAMES))],
+}
+TURNING_TRACKS = {
+    0: [
+        np.array([0.0, 0.0, 0.0]),
+        np.array([0.01, 0.0, 0.001]),
+        np.array([0.018, 0.006, 0.002]),
+        np.array([0.023, 0.014, 0.003]),
+        np.array([0.025, 0.023, 0.004]),
+        np.array([0.024, 0.033, 0.005]),
+    ],
+}
+JUMP_SWITCH_TRACKS = {
+    0: [
+        np.array([0.0, 0.0, 0.0]),
+        np.array([0.01, 0.01, 0.01]),
+        np.array([0.02, 0.02, 0.02]),
+        None,
+        None,
+        None,
+        None,
+    ],
+    1: [
+        None,
+        None,
+        np.array([0.05, 0.05, 0.05]),
+        np.array([0.06, 0.06, 0.06]),
+        np.array([0.07, 0.07, 0.07]),
+        np.array([0.08, 0.08, 0.08]),
+    ],
+}
+BRANCH_SWITCH_TRACKS = {
+    0: [
+        np.array([0.0, 0.0, 0.0]),
+        np.array([0.01, 0.0, 0.001]),
+        np.array([0.018, 0.006, 0.002]),
+        None,
+        None,
+        None,
+    ],
+    1: [
+        None,
+        None,
+        None,
+        np.array([0.024, 0.022, 0.004]),
+        np.array([0.030, 0.032, 0.005]),
+        np.array([0.036, 0.042, 0.006]),
+    ],
+}
+
+
+@dataclass(frozen=True)
+class TrackingSnapshotResult:
+    snapshot: dict[int, list[tuple[int, int, tuple[int, int, int, int], tuple[float, float, float]]]]
+    log: str
+    step_stats: dict[int, tuple[int, int]]
+
+
+def _make_bounds(*, dv_limit: float, dacc: float, dangle: float) -> dict[str, float]:
+    return {
+        "dacc": dacc,
+        "dangle": dangle,
+        "dvxmin": -dv_limit,
+        "dvxmax": dv_limit,
+        "dvymin": -dv_limit,
+        "dvymax": dv_limit,
+        "dvzmin": -dv_limit,
+        "dvzmax": dv_limit,
+    }
+
+
+def _rounded_position(pos: np.ndarray) -> tuple[float, float, float]:
+    return tuple(round(float(value), 5) for value in pos)
+
+
+def _snapshot_graph(snapshot):
+    return {
+        frame: [(prev, next_, pos) for prev, next_, _corres, pos in rows]
+        for frame, rows in snapshot.items()
+    }
+
+
+def _candidate_passes_tracking_gate(
+    prev_pos: np.ndarray,
+    curr_pos: np.ndarray,
+    cand_pos: np.ndarray,
+    bounds: dict[str, float],
+) -> bool:
+    diff_pos = cand_pos - curr_pos
+    if not (
+        bounds["dvxmin"] < diff_pos[0] < bounds["dvxmax"]
+        and bounds["dvymin"] < diff_pos[1] < bounds["dvymax"]
+        and bounds["dvzmin"] < diff_pos[2] < bounds["dvzmax"]
+    ):
+        return False
+
+    pred_pos = track.search_volume_center_moving.py_func(prev_pos, curr_pos)
+    angle, acc = track.angle_acc.py_func(curr_pos, pred_pos, cand_pos)
+    return (acc < bounds["dacc"] and angle < bounds["dangle"]) or (
+        acc < bounds["dacc"] / 10
+    )
+
+
+def _single_track_chain_graph(positions: list[np.ndarray]):
+    return {
+        frame: [
+            (
+                -1 if index == 0 else 0,
+                -2 if index == len(FRAMES) - 1 else 0,
+                _rounded_position(pos),
+            )
+        ]
+        for index, (frame, pos) in enumerate(zip(FRAMES, positions))
+    }
+
+
+def _single_track_delayed_chain_graph(positions: list[np.ndarray]):
+    return {
+        FRAMES[0]: [(-1, -2, _rounded_position(positions[0]))],
+        FRAMES[1]: [(-1, 0, _rounded_position(positions[1]))],
+        FRAMES[2]: [(0, 0, _rounded_position(positions[2]))],
+        FRAMES[3]: [(0, 0, _rounded_position(positions[3]))],
+        FRAMES[4]: [(0, 0, _rounded_position(positions[4]))],
+        FRAMES[5]: [(0, -2, _rounded_position(positions[5]))],
+    }
+
+
+def _single_track_unlinked_graph(positions: list[np.ndarray]):
+    return {
+        frame: [(-1, -2, _rounded_position(pos))]
+        for frame, pos in zip(FRAMES, positions)
+    }
+
+
+def _switch_graph(tracks, *, inherited: bool):
+    first_positions = tracks[0]
+    second_positions = tracks[1]
+    return {
+        FRAMES[0]: [(-1, 0, _rounded_position(first_positions[0]))],
+        FRAMES[1]: [(0, 0, _rounded_position(first_positions[1]))],
+        FRAMES[2]: [(0, 0 if inherited else -2, _rounded_position(first_positions[2]))],
+        FRAMES[3]: [((-1, 0)[inherited], 0, _rounded_position(second_positions[3]))],
+        FRAMES[4]: [(0, 0, _rounded_position(second_positions[4]))],
+        FRAMES[5]: [(0, -2, _rounded_position(second_positions[5]))],
+    }
+
+
+EXPECTED_CONSTANT_DIAGONAL_CHAIN_GRAPH = _single_track_chain_graph(
+    CONSTANT_DIAGONAL_TRACKS[0]
+)
+EXPECTED_CONSTANT_DIAGONAL_REJECT_GRAPH = _single_track_unlinked_graph(
+    CONSTANT_DIAGONAL_TRACKS[0]
+)
+EXPECTED_TURNING_CHAIN_GRAPH = _single_track_delayed_chain_graph(TURNING_TRACKS[0])
+EXPECTED_TURNING_REJECT_GRAPH = _single_track_unlinked_graph(TURNING_TRACKS[0])
+EXPECTED_JUMP_FRESH_START_GRAPH = _switch_graph(JUMP_SWITCH_TRACKS, inherited=False)
+EXPECTED_JUMP_FALSE_LINK_GRAPH = _switch_graph(JUMP_SWITCH_TRACKS, inherited=True)
+EXPECTED_BRANCH_FRESH_START_GRAPH = _switch_graph(BRANCH_SWITCH_TRACKS, inherited=False)
+EXPECTED_BRANCH_FALSE_LINK_GRAPH = _switch_graph(BRANCH_SWITCH_TRACKS, inherited=True)
+EXPECTED_SINGLE_TRACK_LINK_STATS = {frame: (1, 0) for frame in FRAMES[:-1]}
+EXPECTED_SINGLE_TRACK_REJECT_STATS = {frame: (0, 1) for frame in FRAMES[:-1]}
+EXPECTED_TURNING_DELAYED_LINK_STATS = {
+    FRAMES[0]: (0, 1),
+    FRAMES[1]: (1, 0),
+    FRAMES[2]: (1, 0),
+    FRAMES[3]: (1, 0),
+    FRAMES[4]: (1, 0),
+}
+
+
+def _to_native_volume_par(vpar):
+    """Convert Python volume parameters into native VolumeParams."""
+    if NativeVolumeParams is None:
+        raise RuntimeError("optv VolumeParams is not available")
+
+    native_vpar = NativeVolumeParams()
+    native_vpar.set_X_lay(list(vpar.x_lay))
+    native_vpar.set_Zmin_lay(list(vpar.z_min_lay))
+    native_vpar.set_Zmax_lay(list(vpar.z_max_lay))
+    native_vpar.set_cn(vpar.cn)
+    native_vpar.set_cnx(vpar.cnx)
+    native_vpar.set_cny(vpar.cny)
+    native_vpar.set_csumg(vpar.csumg)
+    native_vpar.set_eps0(vpar.eps0)
+    native_vpar.set_corrmin(vpar.corrmin)
+    return native_vpar
+
+
+def _synthetic_track_positions():
+    """Return deterministic per-frame 3D positions for two tracks."""
+    return {
+        0: [np.array([0.00 + 0.01 * index, 0.0, 0.0]) for index in range(len(FRAMES))],
+        1: [np.array([0.02 + 0.01 * index, 0.01, 0.0]) for index in range(len(FRAMES))],
+    }
+
+
+def _write_sequence_range(sequence_path: Path) -> None:
+    lines = sequence_path.read_text(encoding="utf-8").splitlines()
+    lines[-2] = str(FRAMES[0])
+    lines[-1] = str(FRAMES[-1])
+    sequence_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def _parse_tracking_step_stats(log: str) -> dict[int, tuple[int, int]]:
+    return {
+        int(frame): (int(links), int(lost))
+        for frame, links, lost in re.findall(
+            r"step:\s*(\d+),.*?links:\s*(-?\d+),\s*lost:\s*(-?\d+)",
+            log,
+            re.DOTALL,
+        )
+    }
+
+
+def _write_synthetic_tracking_fixture(
+    workdir: Path,
+    tracks: dict[int, list[np.ndarray | None]] | None = None,
+    *,
+    add_distractor: bool = True,
+) -> list[Calibration]:
+    """Write a clean deterministic tracking fixture into a temporary workspace."""
+    source = Path("/home/user/Documents/GitHub/openptv-python/tests/testing_fodder")
+    shutil.copytree(source, workdir)
+    os.chdir(workdir)
+
+    calibrations = [
+        Calibration().from_file(
+            Path(f"cal/sym_cam{cam + 1}.tif.ori"),
+            Path("cal/cam1.tif.addpar"),
+        )
+        for cam in range(3)
+    ]
+
+    os.chdir(workdir / "track")
+    Path("res").mkdir()
+    Path("newpart").mkdir(exist_ok=True)
+    _write_sequence_range(Path("parameters/sequence_newpart.par"))
+
+    cpar = ControlPar(3).from_file(Path("parameters/control_newpart.par"))
+    tracks = tracks or _synthetic_track_positions()
+
+    for frame_index, frame_num in enumerate(FRAMES):
+        frame = Frame(3, 16)
+        cam_targets = [[] for _ in range(3)]
+        correspond_indices = {
+            track_id: [CORRES_NONE] * 4
+            for track_id, positions in tracks.items()
+            if positions[frame_index] is not None
+        }
+
+        for track_id, positions in tracks.items():
+            pos = positions[frame_index]
+            if pos is None:
+                continue
+            projections = [
+                image_coordinates(np.array([pos]), cal, cpar.mm)[0]
+                for cal in calibrations
+            ]
+            for cam, xy in enumerate(projections):
+                px, py = metric_to_pixel(xy[0], xy[1], cpar)
+                cam_targets[cam].append(
+                    Target(
+                        pnr=len(cam_targets[cam]),
+                        x=float(px),
+                        y=float(py),
+                        n=10,
+                        nx=3,
+                        ny=3,
+                        sumg=100,
+                        tnr=track_id,
+                    )
+                )
+
+        # Keep one unmatched distractor near the search area in a single camera.
+        if add_distractor:
+            cam_targets[0].append(
+                Target(
+                    pnr=99,
+                    x=960.0,
+                    y=540.0 + frame_index,
+                    n=8,
+                    nx=2,
+                    ny=2,
+                    sumg=50,
+                    tnr=CORRES_NONE,
+                )
+            )
+
+        for cam in range(3):
+            cam_targets[cam].sort(key=lambda target: target.y)
+            write_targets(
+                cam_targets[cam],
+                len(cam_targets[cam]),
+                f"newpart/cam{cam + 1}.%05d",
+                frame_num,
+            )
+            for idx, target in enumerate(cam_targets[cam]):
+                if target.tnr in correspond_indices:
+                    correspond_indices[target.tnr][cam] = idx
+
+        active_track_ids = sorted(correspond_indices)
+        frame.num_parts = len(active_track_ids)
+        frame.correspond = np.recarray((frame.num_parts,), dtype=frame.correspond.dtype)
+        frame.path_info = [Pathinfo() for _ in range(frame.num_parts)]
+
+        for row_index, track_id in enumerate(active_track_ids):
+            frame.correspond[row_index].nr = track_id + 1
+            frame.correspond[row_index].p = np.array(
+                correspond_indices[track_id],
+                dtype=np.int32,
+            )
+            frame.path_info[row_index].x = tracks[track_id][frame_index]
+
+        write_path_frame(
+            frame.correspond,
+            frame.path_info,
+            frame.num_parts,
+            "res/particles",
+            "res/linkage",
+            "res/whatever",
+            frame_num,
+        )
+
+    return calibrations
+
+
+def _snapshot_tracking_outputs() -> dict[int, list[tuple[int, int, tuple[int, int, int, int], tuple[float, float, float]]]]:
+    """Read the written tracking outputs into a stable frame-by-frame snapshot."""
+    snapshots = {}
+    for frame in FRAMES:
+        correspond, path_info = read_path_frame(
+            "res/particles",
+            "res/linkage",
+            "res/whatever",
+            frame,
+        )
+        snapshots[frame] = [
+            (
+                int(path.prev_frame),
+                int(path.next_frame),
+                tuple(int(value) for value in correspond[idx].p),
+                tuple(round(float(value), 5) for value in path.x),
+            )
+            for idx, path in enumerate(path_info)
+        ]
+    return snapshots
+
+
+def _run_python_tracking_snapshot(
+    mode: str,
+    bounds: dict[str, float],
+    *,
+    tracks: dict[int, list[np.ndarray | None]] | None = None,
+    add_distractor: bool = True,
+) -> TrackingSnapshotResult:
+    """Run the Python tracker in either compiled or patched-Python mode."""
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        workdir = Path(tmp_dir) / "testing_fodder"
+        original_cwd = Path.cwd()
+        try:
+            calibrations = _write_synthetic_tracking_fixture(
+                workdir,
+                tracks=tracks,
+                add_distractor=add_distractor,
+            )
+            run = tr_new(
+                Path("parameters/sequence_newpart.par"),
+                Path("parameters/track.par"),
+                Path("parameters/criteria.par"),
+                Path("parameters/control_newpart.par"),
+                4,
+                16,
+                "res/particles",
+                "res/linkage",
+                "res/whatever",
+                calibrations,
+                0.1,
+            )
+            run.tpar = run.tpar._replace(add=0, **bounds)
+
+            with ExitStack() as stack:
+                if mode == "python":
+                    stack.enter_context(
+                        patch.object(
+                            track,
+                            "search_volume_center_moving",
+                            track.search_volume_center_moving.py_func,
+                        )
+                    )
+                    stack.enter_context(
+                        patch.object(
+                            track,
+                            "pos3d_in_bounds",
+                            track.pos3d_in_bounds.py_func,
+                        )
+                    )
+                    stack.enter_context(
+                        patch.object(
+                            track,
+                            "angle_acc",
+                            track.angle_acc.py_func,
+                        )
+                    )
+
+                def run_tracker() -> None:
+                    track_forward_start(run)
+                    for step in range(run.seq_par.first, run.seq_par.last):
+                        trackcorr_c_loop(run, step)
+                    trackcorr_c_finish(run, run.seq_par.last)
+
+                log_buffer = io.StringIO()
+                with redirect_stdout(log_buffer):
+                    run_tracker()
+                log = log_buffer.getvalue()
+            return TrackingSnapshotResult(
+                snapshot=_snapshot_tracking_outputs(),
+                log=log,
+                step_stats=_parse_tracking_step_stats(log),
+            )
+        finally:
+            os.chdir(original_cwd)
+
+
+def _run_native_tracking_snapshot(
+    bounds: dict[str, float],
+    *,
+    tracks: dict[int, list[np.ndarray | None]] | None = None,
+    add_distractor: bool = True,
+):
+    """Run the native optv tracker on the same synthetic fixture."""
+    if not HAS_NATIVE_TRACKING or NativeTracker is None or NativeTrackingParams is None:
+        raise RuntimeError("optv Tracker is not available")
+
+    with tempfile.TemporaryDirectory() as tmp_dir:
+        workdir = Path(tmp_dir) / "testing_fodder"
+        original_cwd = Path.cwd()
+        try:
+            calibrations = _write_synthetic_tracking_fixture(
+                workdir,
+                tracks=tracks,
+                add_distractor=add_distractor,
+            )
+            cpar = ControlPar(3).from_file(Path("parameters/control_newpart.par"))
+            cpar.img_base_name = [f"newpart/cam{cam + 1}." for cam in range(3)]
+            vpar = read_volume_par(Path("parameters/criteria.par"))
+
+            native_tpar = NativeTrackingParams()
+            native_tpar.read_track_par("parameters/track.par")
+            native_tpar.set_add(0)
+            native_tpar.set_dacc(bounds["dacc"])
+            native_tpar.set_dangle(bounds["dangle"])
+            native_tpar.set_dvxmin(bounds["dvxmin"])
+            native_tpar.set_dvxmax(bounds["dvxmax"])
+            native_tpar.set_dvymin(bounds["dvymin"])
+            native_tpar.set_dvymax(bounds["dvymax"])
+            native_tpar.set_dvzmin(bounds["dvzmin"])
+            native_tpar.set_dvzmax(bounds["dvzmax"])
+
+            native_spar = NativeSequenceParams(num_cams=3)
+            native_spar.read_sequence_par("parameters/sequence_newpart.par", 3)
+            native_spar.set_first(FRAMES[0])
+            native_spar.set_last(FRAMES[-1])
+            for cam in range(3):
+                native_spar.set_img_base_name(cam, f"newpart/cam{cam + 1}.")
+
+            tracker = NativeTracker(
+                to_native_control_par(cpar),
+                _to_native_volume_par(vpar),
+                native_tpar,
+                native_spar,
+                [to_native_calibration(cal) for cal in calibrations],
+                naming={
+                    "corres": "res/particles",
+                    "linkage": "res/linkage",
+                    "prio": "res/whatever",
+                },
+                flatten_tol=0.1,
+            )
+            tracker.full_forward()
+            return TrackingSnapshotResult(
+                snapshot=_snapshot_tracking_outputs(),
+                log="",
+                step_stats={},
+            )
+        finally:
+            os.chdir(original_cwd)
+
+
+class TestTrackingGroundTruth(unittest.TestCase):
+    """Ground-truth tracking comparisons across Python and native backends."""
+
+    def assert_python_modes_match_graph(
+        self,
+        *,
+        tracks,
+        bounds,
+        expected_graph,
+        expected_step_stats=None,
+        add_distractor=False,
+    ):
+        compiled_snapshot = _run_python_tracking_snapshot(
+            "python+numba",
+            bounds,
+            tracks=tracks,
+            add_distractor=add_distractor,
+        )
+        python_snapshot = _run_python_tracking_snapshot(
+            "python",
+            bounds,
+            tracks=tracks,
+            add_distractor=add_distractor,
+        )
+
+        self.assertEqual(_snapshot_graph(compiled_snapshot.snapshot), expected_graph)
+        self.assertEqual(_snapshot_graph(python_snapshot.snapshot), expected_graph)
+        self.assertEqual(compiled_snapshot.snapshot, python_snapshot.snapshot)
+        if expected_step_stats is not None:
+            self.assertEqual(compiled_snapshot.step_stats, expected_step_stats)
+            self.assertEqual(python_snapshot.step_stats, expected_step_stats)
+        self.assertNotRegex(compiled_snapshot.log, BAD_FILE_LOOKUP_PATTERN)
+        self.assertNotRegex(python_snapshot.log, BAD_FILE_LOOKUP_PATTERN)
+
+    def test_tracking_exact_link_graph_matches_ground_truth_across_backends(self):
+        """Match the exact permissive link graph across Python, Numba, and optv."""
+        expected = EXPECTED_PERMISSIVE_SNAPSHOT
+        compiled_snapshot = _run_python_tracking_snapshot("python+numba", PERMISSIVE_BOUNDS)
+        python_snapshot = _run_python_tracking_snapshot("python", PERMISSIVE_BOUNDS)
+
+        self.assertEqual(compiled_snapshot.snapshot, expected)
+        self.assertEqual(python_snapshot.snapshot, expected)
+        self.assertEqual(compiled_snapshot.snapshot, python_snapshot.snapshot)
+        self.assertEqual(compiled_snapshot.step_stats, EXPECTED_PERMISSIVE_STEP_STATS)
+        self.assertEqual(python_snapshot.step_stats, EXPECTED_PERMISSIVE_STEP_STATS)
+        self.assertNotRegex(compiled_snapshot.log, BAD_FILE_LOOKUP_PATTERN)
+        self.assertNotRegex(python_snapshot.log, BAD_FILE_LOOKUP_PATTERN)
+
+        if HAS_OPTV and HAS_NATIVE_TRACKING:
+            native_snapshot = _run_native_tracking_snapshot(PERMISSIVE_BOUNDS)
+            self.assertEqual(native_snapshot.snapshot, expected)
+            self.assertEqual(native_snapshot.snapshot, compiled_snapshot.snapshot)
+
+    def test_tracking_strict_bounds_reject_links_by_design(self):
+        """Reject links consistently across backends when the velocity window excludes the motion."""
+        expected = EXPECTED_STRICT_SNAPSHOT
+        compiled_snapshot = _run_python_tracking_snapshot("python+numba", STRICT_BOUNDS)
+        python_snapshot = _run_python_tracking_snapshot("python", STRICT_BOUNDS)
+
+        self.assertEqual(compiled_snapshot.snapshot, expected)
+        self.assertEqual(python_snapshot.snapshot, expected)
+        self.assertEqual(compiled_snapshot.snapshot, python_snapshot.snapshot)
+        self.assertEqual(compiled_snapshot.step_stats, EXPECTED_STRICT_STEP_STATS)
+        self.assertEqual(python_snapshot.step_stats, EXPECTED_STRICT_STEP_STATS)
+        self.assertNotRegex(compiled_snapshot.log, BAD_FILE_LOOKUP_PATTERN)
+        self.assertNotRegex(python_snapshot.log, BAD_FILE_LOOKUP_PATTERN)
+
+    def test_tracking_velocity_window_has_valid_range_between_reject_and_false_link(self):
+        """A symmetric 3D velocity window must be wide enough for truth but narrow enough to block jumps."""
+        self.assert_python_modes_match_graph(
+            tracks=CONSTANT_DIAGONAL_TRACKS,
+            bounds=_make_bounds(dv_limit=0.005, dacc=0.2, dangle=100.0),
+            expected_graph=EXPECTED_CONSTANT_DIAGONAL_REJECT_GRAPH,
+            expected_step_stats=EXPECTED_SINGLE_TRACK_REJECT_STATS,
+        )
+        self.assert_python_modes_match_graph(
+            tracks=CONSTANT_DIAGONAL_TRACKS,
+            bounds=_make_bounds(dv_limit=0.015, dacc=0.2, dangle=100.0),
+            expected_graph=EXPECTED_CONSTANT_DIAGONAL_CHAIN_GRAPH,
+            expected_step_stats=EXPECTED_SINGLE_TRACK_LINK_STATS,
+        )
+        self.assertFalse(
+            _candidate_passes_tracking_gate(
+                np.array([0.01, 0.01, 0.01]),
+                np.array([0.02, 0.02, 0.02]),
+                np.array([0.05, 0.05, 0.04]),
+                _make_bounds(dv_limit=0.015, dacc=0.2, dangle=100.0),
+            )
+        )
+        self.assertTrue(
+            _candidate_passes_tracking_gate(
+                np.array([0.01, 0.01, 0.01]),
+                np.array([0.02, 0.02, 0.02]),
+                np.array([0.05, 0.05, 0.04]),
+                _make_bounds(dv_limit=0.05, dacc=0.2, dangle=100.0),
+            )
+        )
+
+    def test_tracking_dacc_has_valid_range_between_reject_and_false_link(self):
+        """Acceleration bounds should admit smooth curvature but still reject identity-switch jumps."""
+        self.assert_python_modes_match_graph(
+            tracks=TURNING_TRACKS,
+            bounds=_make_bounds(dv_limit=0.02, dacc=0.002, dangle=40.0),
+            expected_graph=EXPECTED_TURNING_REJECT_GRAPH,
+            expected_step_stats=EXPECTED_SINGLE_TRACK_REJECT_STATS,
+        )
+        self.assert_python_modes_match_graph(
+            tracks=TURNING_TRACKS,
+            bounds=_make_bounds(dv_limit=0.02, dacc=0.02, dangle=40.0),
+            expected_graph=EXPECTED_TURNING_CHAIN_GRAPH,
+            expected_step_stats=EXPECTED_TURNING_DELAYED_LINK_STATS,
+        )
+        self.assertFalse(
+            _candidate_passes_tracking_gate(
+                np.array([0.01, 0.01, 0.01]),
+                np.array([0.02, 0.02, 0.02]),
+                np.array([0.05, 0.05, 0.04]),
+                _make_bounds(dv_limit=0.05, dacc=0.02, dangle=100.0),
+            )
+        )
+        self.assertTrue(
+            _candidate_passes_tracking_gate(
+                np.array([0.01, 0.01, 0.01]),
+                np.array([0.02, 0.02, 0.02]),
+                np.array([0.05, 0.05, 0.04]),
+                _make_bounds(dv_limit=0.05, dacc=0.06, dangle=100.0),
+            )
+        )
+
+    def test_tracking_dangle_has_valid_range_between_reject_and_false_link(self):
+        """Angular bounds should admit smooth turning but still reject a sharp branch switch."""
+        self.assert_python_modes_match_graph(
+            tracks=TURNING_TRACKS,
+            bounds=_make_bounds(dv_limit=0.02, dacc=0.02, dangle=10.0),
+            expected_graph=EXPECTED_TURNING_REJECT_GRAPH,
+            expected_step_stats=EXPECTED_SINGLE_TRACK_REJECT_STATS,
+        )
+        self.assert_python_modes_match_graph(
+            tracks=TURNING_TRACKS,
+            bounds=_make_bounds(dv_limit=0.02, dacc=0.02, dangle=30.0),
+            expected_graph=EXPECTED_TURNING_CHAIN_GRAPH,
+            expected_step_stats=EXPECTED_TURNING_DELAYED_LINK_STATS,
+        )
+        self.assertFalse(
+            _candidate_passes_tracking_gate(
+                np.array([0.01, 0.0, 0.001]),
+                np.array([0.018, 0.006, 0.002]),
+                np.array([0.024, 0.022, 0.004]),
+                _make_bounds(dv_limit=0.02, dacc=0.02, dangle=30.0),
+            )
+        )
+        self.assertTrue(
+            _candidate_passes_tracking_gate(
+                np.array([0.01, 0.0, 0.001]),
+                np.array([0.018, 0.006, 0.002]),
+                np.array([0.024, 0.022, 0.004]),
+                _make_bounds(dv_limit=0.02, dacc=0.02, dangle=40.0),
+            )
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file

From 4f1765b86acb696dbea98856155a60cdc09ad6f0 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sat, 14 Mar 2026 23:41:09 +0200
Subject: [PATCH 20/27] fixed tests

---
 openptv_python/track.py             |  9 +++--
 tests/test_tracking.py              | 11 +++++
 tests/test_tracking_ground_truth.py | 62 ++++++++++++++++++++++-------
 3 files changed, 65 insertions(+), 17 deletions(-)

diff --git a/openptv_python/track.py b/openptv_python/track.py
index 8b19443..f9e2697 100644
--- a/openptv_python/track.py
+++ b/openptv_python/track.py
@@ -261,9 +261,12 @@ def angle_acc(start: np.ndarray, pred: np.ndarray, cand: np.ndarray) -> np.ndarr
         norm_start_pred = np.linalg.norm(start - pred)
         norm_start_cand = np.linalg.norm(start - cand)
 
-        angle = (200.0 / np.pi) * np.arccos(
-            dot_product / (norm_start_pred * norm_start_cand)
-        )
+        if norm_start_pred == 0.0 or norm_start_cand == 0.0:
+            angle = 0.0
+        else:
+            cosine = dot_product / (norm_start_pred * norm_start_cand)
+            cosine = min(1.0, max(-1.0, cosine))
+            angle = (200.0 / np.pi) * np.arccos(cosine)
 
     return np.array([angle, acc])
 
diff --git a/tests/test_tracking.py b/tests/test_tracking.py
index b4a0afe..b329788 100644
--- a/tests/test_tracking.py
+++ b/tests/test_tracking.py
@@ -180,6 +180,17 @@ def test_angle_acc(self):
         )
         self.assertTrue(isclose(acc, 0.0, rel_tol=EPS), f"Expected 0.0 but found {acc}")
 
+        cand = np.array([2.0, 2.0, 2.0])
+
+        angle, acc = angle_acc(start, pred, cand)
+        self.assertTrue(
+            isclose(angle, 0.0, rel_tol=EPS), f"Expected 0.0 but found {angle}"
+        )
+        self.assertTrue(
+            isclose(acc, np.sqrt(3.0), rel_tol=EPS),
+            f"Expected {np.sqrt(3.0)} but found {acc}",
+        )
+
         cand = vec_scalar_mul(pred, -1)
 
         angle, acc = angle_acc(start, pred, cand)
diff --git a/tests/test_tracking_ground_truth.py b/tests/test_tracking_ground_truth.py
index afb7847..026954b 100644
--- a/tests/test_tracking_ground_truth.py
+++ b/tests/test_tracking_ground_truth.py
@@ -1,3 +1,4 @@
+import io
 import os
 import re
 import shutil
@@ -5,7 +6,6 @@
 import unittest
 from contextlib import ExitStack, redirect_stdout
 from dataclasses import dataclass
-import io
 from pathlib import Path
 from unittest.mock import patch
 
@@ -18,7 +18,11 @@
 from openptv_python.constants import CORRES_NONE
 from openptv_python.imgcoord import image_coordinates
 from openptv_python.parameters import ControlPar, read_volume_par
-from openptv_python.track import track_forward_start, trackcorr_c_finish, trackcorr_c_loop
+from openptv_python.track import (
+    track_forward_start,
+    trackcorr_c_finish,
+    trackcorr_c_loop,
+)
 from openptv_python.tracking_frame_buf import (
     Frame,
     Pathinfo,
@@ -67,12 +71,30 @@
     "dvzmax": 0.005,
 }
 EXPECTED_PERMISSIVE_SNAPSHOT = {
-    10001: [(-1, 0, (2, 1, 1, -1), (0.0, 0.0, 0.0)), (-1, 1, (1, 0, 0, -1), (0.02, 0.01, 0.0))],
-    10002: [(0, 0, (2, 1, 1, -1), (0.01, 0.0, 0.0)), (1, 1, (1, 0, 0, -1), (0.03, 0.01, 0.0))],
-    10003: [(0, 0, (2, 1, 1, -1), (0.02, 0.0, 0.0)), (1, 1, (1, 0, 0, -1), (0.04, 0.01, 0.0))],
-    10004: [(0, 0, (2, 1, 1, -1), (0.03, 0.0, 0.0)), (1, 1, (1, 0, 0, -1), (0.05, 0.01, 0.0))],
-    10005: [(0, 0, (2, 1, 1, -1), (0.04, 0.0, 0.0)), (1, 1, (1, 0, 0, -1), (0.06, 0.01, 0.0))],
-    10006: [(0, -2, (2, 1, 1, -1), (0.05, 0.0, 0.0)), (1, -2, (1, 0, 0, -1), (0.07, 0.01, 0.0))],
+    10001: [
+        (-1, 0, (2, 1, 1, -1), (0.0, 0.0, 0.0)),
+        (-1, 1, (1, 0, 0, -1), (0.02, 0.01, 0.0)),
+    ],
+    10002: [
+        (0, 0, (2, 1, 1, -1), (0.01, 0.0, 0.0)),
+        (1, 1, (1, 0, 0, -1), (0.03, 0.01, 0.0)),
+    ],
+    10003: [
+        (0, 0, (2, 1, 1, -1), (0.02, 0.0, 0.0)),
+        (1, 1, (1, 0, 0, -1), (0.04, 0.01, 0.0)),
+    ],
+    10004: [
+        (0, 0, (2, 1, 1, -1), (0.03, 0.0, 0.0)),
+        (1, 1, (1, 0, 0, -1), (0.05, 0.01, 0.0)),
+    ],
+    10005: [
+        (0, 0, (2, 1, 1, -1), (0.04, 0.0, 0.0)),
+        (1, 1, (1, 0, 0, -1), (0.06, 0.01, 0.0)),
+    ],
+    10006: [
+        (0, -2, (2, 1, 1, -1), (0.05, 0.0, 0.0)),
+        (1, -2, (1, 0, 0, -1), (0.07, 0.01, 0.0)),
+    ],
 }
 EXPECTED_STRICT_SNAPSHOT = {
     frame: [
@@ -86,7 +108,10 @@
 BAD_FILE_LOOKUP_PATTERN = re.compile(r"Can't open ascii file|\d{10}_targets")
 
 CONSTANT_DIAGONAL_TRACKS = {
-    0: [np.array([0.01 * index, 0.01 * index, 0.01 * index]) for index in range(len(FRAMES))],
+    0: [
+        np.array([0.01 * index, 0.01 * index, 0.01 * index])
+        for index in range(len(FRAMES))
+    ],
 }
 TURNING_TRACKS = {
     0: [
@@ -139,7 +164,10 @@
 
 @dataclass(frozen=True)
 class TrackingSnapshotResult:
-    snapshot: dict[int, list[tuple[int, int, tuple[int, int, int, int], tuple[float, float, float]]]]
+    snapshot: dict[
+        int,
+        list[tuple[int, int, tuple[int, int, int, int], tuple[float, float, float]]],
+    ]
     log: str
     step_stats: dict[int, tuple[int, int]]
 
@@ -412,7 +440,9 @@ def _write_synthetic_tracking_fixture(
     return calibrations
 
 
-def _snapshot_tracking_outputs() -> dict[int, list[tuple[int, int, tuple[int, int, int, int], tuple[float, float, float]]]]:
+def _snapshot_tracking_outputs() -> dict[
+    int, list[tuple[int, int, tuple[int, int, int, int], tuple[float, float, float]]]
+]:
     """Read the written tracking outputs into a stable frame-by-frame snapshot."""
     snapshots = {}
     for frame in FRAMES:
@@ -611,7 +641,9 @@ def assert_python_modes_match_graph(
     def test_tracking_exact_link_graph_matches_ground_truth_across_backends(self):
         """Match the exact permissive link graph across Python, Numba, and optv."""
         expected = EXPECTED_PERMISSIVE_SNAPSHOT
-        compiled_snapshot = _run_python_tracking_snapshot("python+numba", PERMISSIVE_BOUNDS)
+        compiled_snapshot = _run_python_tracking_snapshot(
+            "python+numba", PERMISSIVE_BOUNDS
+        )
         python_snapshot = _run_python_tracking_snapshot("python", PERMISSIVE_BOUNDS)
 
         self.assertEqual(compiled_snapshot.snapshot, expected)
@@ -641,7 +673,9 @@ def test_tracking_strict_bounds_reject_links_by_design(self):
         self.assertNotRegex(compiled_snapshot.log, BAD_FILE_LOOKUP_PATTERN)
         self.assertNotRegex(python_snapshot.log, BAD_FILE_LOOKUP_PATTERN)
 
-    def test_tracking_velocity_window_has_valid_range_between_reject_and_false_link(self):
+    def test_tracking_velocity_window_has_valid_range_between_reject_and_false_link(
+        self,
+    ):
         """A symmetric 3D velocity window must be wide enough for truth but narrow enough to block jumps."""
         self.assert_python_modes_match_graph(
             tracks=CONSTANT_DIAGONAL_TRACKS,
@@ -736,4 +770,4 @@ def test_tracking_dangle_has_valid_range_between_reject_and_false_link(self):
 
 
 if __name__ == "__main__":
-    unittest.main()
\ No newline at end of file
+    unittest.main()

From eeaedc8380566ae80df4d8870d857dfbea2a2fca Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sun, 15 Mar 2026 00:56:18 +0200
Subject: [PATCH 21/27] Potential fix for pull request finding

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index 7cee62b..2a48625 100644
--- a/Makefile
+++ b/Makefile
@@ -2,7 +2,7 @@ PROJECT := openptv_python
 CONDA := conda
 CONDAFLAGS :=
 COV_REPORT := html
-PYTHON ?= .venv/bin/python
+PYTHON ?= python
 
 default: qa unit-tests type-check
 

From cf81b7ada9db356cdb9e9fc7d0a267096afab65f Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sun, 15 Mar 2026 01:15:43 +0200
Subject: [PATCH 22/27] uv sync vs uv sync --extra dev

---
 .github/workflows/on-push.yml |    6 +-
 .github/workflows/static.yml  |   14 +-
 README.html                   |  679 -------------------
 README.md                     |   73 +-
 ci/environment-ci.yml         |    2 +
 docs/index.md                 |    3 +-
 plans.md                      |    2 -
 pyproject.toml                |   39 +-
 uv.lock                       | 1175 +++++++++++++++++----------------
 9 files changed, 703 insertions(+), 1290 deletions(-)
 delete mode 100644 README.html
 delete mode 100644 plans.md

diff --git a/.github/workflows/on-push.yml b/.github/workflows/on-push.yml
index b04fbfb..6251794 100644
--- a/.github/workflows/on-push.yml
+++ b/.github/workflows/on-push.yml
@@ -25,7 +25,7 @@ jobs:
     - uses: actions/checkout@v6
     - uses: actions/setup-python@v6
       with:
-        python-version: 3.x
+        python-version: '3.12'
     - uses: pre-commit/action@v3.0.1
 
   combine-environments:
@@ -35,7 +35,7 @@ jobs:
     - uses: actions/checkout@v6
     - uses: actions/setup-python@v6
       with:
-        python-version: 3.x
+        python-version: '3.12'
     - name: Install conda-merge
       run: |
         python -m pip install conda-merge
@@ -55,7 +55,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.11', '3.12']
+        python-version: ['3.12', '3.13']
 
     steps:
     - uses: actions/checkout@v6
diff --git a/.github/workflows/static.yml b/.github/workflows/static.yml
index 6d12253..2391e33 100644
--- a/.github/workflows/static.yml
+++ b/.github/workflows/static.yml
@@ -31,13 +31,23 @@ jobs:
     steps:
     - name: Checkout
       uses: actions/checkout@v4
+    - name: Setup Python
+      uses: actions/setup-python@v6
+      with:
+        python-version: '3.12'
+    - name: Install documentation dependencies
+      run: |
+        python -m pip install --upgrade pip
+        python -m pip install -e ".[docs]"
+    - name: Build documentation
+      run: |
+        make docs-build
     - name: Setup Pages
       uses: actions/configure-pages@v4
     - name: Upload artifact
       uses: actions/upload-pages-artifact@v3
       with:
-          # Upload entire repository
-        path: '.'
+        path: docs/_build/html
     - name: Deploy to GitHub Pages
       id: deployment
       uses: actions/deploy-pages@v4
diff --git a/README.html b/README.html
deleted file mode 100644
index 7dbf403..0000000
--- a/README.html
+++ /dev/null
@@ -1,679 +0,0 @@
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-
-
-
-
-<section id="openptv-python" class="level1">
-<h1>openptv-python</h1>
-<p>Python version of the OpenPTV library - this is <em>a work in progress</em></p>
-<section id="what-this-repo-provides" class="level2">
-<h2 class="anchored" data-anchor-id="what-this-repo-provides">What This Repo Provides</h2>
-<p><code>openptv-python</code> keeps the Python API as the main interface and combines three execution modes behind that API:</p>
-<ul>
-<li>Pure Python: the reference implementation and the easiest path for reading, debugging, and extending the code.</li>
-<li>Python + Numba: several hot kernels are JIT-compiled automatically on first use, so the Python implementation still benefits from acceleration.</li>
-<li>Native <code>optv</code> bindings: selected operations reuse the native OpenPTV implementation when the <code>optv</code> package is available.</li>
-</ul>
-<p>At the moment, automatic native delegation is implemented for image preprocessing and full-frame target recognition. The rest of the library keeps the same Python API and remains usable even when those native paths are not in use.</p>
-</section>
-<section id="how-this-is-started" class="level2">
-<h2 class="anchored" data-anchor-id="how-this-is-started">How this is started</h2>
-<p>This work started from the https://github.com/OpenPTV/openptv/tree/pure_python branch. It’s a long-standing idea to convert all the C code to Python and now it’s possible with ChatGPT to save a lot of typing time.</p>
-<p>This repo is created using a <em>cookiecutter</em> and the rest of the readme describes the way to work with this structure</p>
-</section>
-<section id="supported-python-versions" class="level2">
-<h2 class="anchored" data-anchor-id="supported-python-versions">Supported Python Versions</h2>
-<p>The project currently supports Python <code>&gt;=3.12,&lt;3.14</code>.</p>
-</section>
-<section id="installation" class="level2">
-<h2 class="anchored" data-anchor-id="installation">Installation</h2>
-<section id="recommended-uv" class="level3">
-<h3 class="anchored" data-anchor-id="recommended-uv">Recommended: uv</h3>
-<p>Create the environment and install all project dependencies:</p>
-<div class="sourceCode" id="cb1"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="ex">uv</span> venv</span>
-<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="bu">source</span> .venv/bin/activate</span>
-<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="ex">uv</span> sync</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<p>This is the simplest way to get the current default stack, including NumPy, SciPy, Numba, and the <code>optv</code> native bindings when they are available for your platform and Python version.</p>
-</section>
-<section id="alternative-conda-pip" class="level3">
-<h3 class="anchored" data-anchor-id="alternative-conda-pip">Alternative: conda + pip</h3>
-<div class="sourceCode" id="cb2"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="ex">conda</span> create <span class="at">-n</span> openptv-python <span class="at">-c</span> conda-forge python=3.12</span>
-<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a><span class="ex">conda</span> activate openptv-python</span>
-<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a><span class="ex">pip</span> install <span class="at">-e</span> .</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</section>
-<section id="what-gets-installed" class="level3">
-<h3 class="anchored" data-anchor-id="what-gets-installed">What gets installed</h3>
-<ul>
-<li><code>numba</code> is part of the default dependency set and accelerates selected Python kernels automatically after the first call.</li>
-<li><code>optv</code> is part of the default dependency set and enables native interop on supported platforms.</li>
-<li>The public API stays the same regardless of which backend is active.</li>
-</ul>
-</section>
-</section>
-<section id="backend-behavior" class="level2">
-<h2 class="anchored" data-anchor-id="backend-behavior">Backend Behavior</h2>
-<section id="pure-python-backend" class="level3">
-<h3 class="anchored" data-anchor-id="pure-python-backend">Pure Python backend</h3>
-<p>This is the base implementation for the whole library. It is always the source of truth for the Python API and remains the fallback behavior for code paths that are not delegated to <code>optv</code>.</p>
-</section>
-<section id="python-numba-backend" class="level3">
-<h3 class="anchored" data-anchor-id="python-numba-backend">Python + Numba backend</h3>
-<p>Numba accelerates selected computational kernels inside the Python implementation. This is automatic; there is no separate API to enable it. Expect the first call to a JIT-compiled function to be slower due to compilation, with later calls running faster.</p>
-</section>
-<section id="native-optv-backend" class="level3">
-<h3 class="anchored" data-anchor-id="native-optv-backend">Native <code>optv</code> backend</h3>
-<p>When <code>optv</code> imports successfully, <code>openptv-python</code> automatically reuses native implementations for:</p>
-<ul>
-<li>image preprocessing</li>
-<li>full-frame target recognition / segmentation</li>
-</ul>
-<p>These native paths are validated against the Python implementation by parity tests, so results stay backend-independent.</p>
-</section>
-<section id="backend-capability-table" class="level3">
-<h3 class="anchored" data-anchor-id="backend-capability-table">Backend Capability Table</h3>
-<table class="caption-top table">
-<colgroup>
-<col style="width: 25%">
-<col style="width: 25%">
-<col style="width: 25%">
-<col style="width: 25%">
-</colgroup>
-<thead>
-<tr class="header">
-<th>Operation</th>
-<th>Pure Python</th>
-<th>Python + Numba</th>
-<th>Native <code>optv</code></th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td>Image preprocessing</td>
-<td>Yes</td>
-<td>Yes</td>
-<td>Yes, automatic delegation</td>
-</tr>
-<tr class="even">
-<td>Target recognition / segmentation</td>
-<td>Yes</td>
-<td>Yes</td>
-<td>Yes, automatic delegation</td>
-</tr>
-<tr class="odd">
-<td>Point reconstruction</td>
-<td>Yes</td>
-<td>Partial internal kernels</td>
-<td>Not used by default</td>
-</tr>
-<tr class="even">
-<td>Correspondence search / stereo matching</td>
-<td>Yes</td>
-<td>Partial internal kernels</td>
-<td>Not used by default</td>
-</tr>
-<tr class="odd">
-<td>Tracking</td>
-<td>Yes</td>
-<td>Partial internal kernels</td>
-<td>Not used by default</td>
-</tr>
-<tr class="even">
-<td>Sequence parameter I/O</td>
-<td>Yes</td>
-<td>No</td>
-<td>Available in native bindings</td>
-</tr>
-</tbody>
-</table>
-<p><code>Not used by default</code> means the native path exists in benchmarks or conversion helpers, but the regular <code>openptv-python</code> runtime path still uses the Python implementation unless that operation is explicitly integrated later.</p>
-</section>
-</section>
-<section id="getting-started" class="level2">
-<h2 class="anchored" data-anchor-id="getting-started">Getting Started</h2>
-<section id="install-the-project" class="level3">
-<h3 class="anchored" data-anchor-id="install-the-project">1. Install the project</h3>
-<p>Use one of the installation methods above.</p>
-</section>
-<section id="verify-imports" class="level3">
-<h3 class="anchored" data-anchor-id="verify-imports">2. Verify imports</h3>
-<div class="sourceCode" id="cb3"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="ex">uv</span> run python <span class="at">-</span> <span class="op">&lt;&lt;'PY'</span></span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="st">import openptv_python</span></span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="st">import numba</span></span>
-<span id="cb3-4"><a href="#cb3-4" aria-hidden="true" tabindex="-1"></a><span class="st">import optv</span></span>
-<span id="cb3-5"><a href="#cb3-5" aria-hidden="true" tabindex="-1"></a></span>
-<span id="cb3-6"><a href="#cb3-6" aria-hidden="true" tabindex="-1"></a><span class="st">print("openptv_python ok")</span></span>
-<span id="cb3-7"><a href="#cb3-7" aria-hidden="true" tabindex="-1"></a><span class="st">print("numba ok", numba.__version__)</span></span>
-<span id="cb3-8"><a href="#cb3-8" aria-hidden="true" tabindex="-1"></a><span class="st">print("optv ok", optv.__version__)</span></span>
-<span id="cb3-9"><a href="#cb3-9" aria-hidden="true" tabindex="-1"></a><span class="op">PY</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</section>
-<section id="start-using-the-python-api" class="level3">
-<h3 class="anchored" data-anchor-id="start-using-the-python-api">3. Start using the Python API</h3>
-<div class="sourceCode" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="op">&gt;&gt;&gt;</span> <span class="im">import</span> openptv_python</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</section>
-<section id="run-the-test-suite" class="level3">
-<h3 class="anchored" data-anchor-id="run-the-test-suite">4. Run the test suite</h3>
-<div class="sourceCode" id="cb5"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="ex">uv</span> run make</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-<p>Stress and performance tests are part of the default suite now. If you need a faster validation pass locally, you can skip them explicitly:</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="va">OPENPTV_SKIP_STRESS_BENCHMARKS</span><span class="op">=</span>1 <span class="ex">uv</span> run make</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
-</section>
-<section id="workflow-for-developerscontributors" class="level3">
-<h3 class="anchored" data-anchor-id="workflow-for-developerscontributors">Workflow for developers/contributors</h3>
-<p>For the best experience create a new conda environment (e.g.&nbsp;DEVELOP) with Python 3.12:</p>
-<pre><code>conda create -n openptv-python -c conda-forge python=3.12
-conda activate openptv-python</code></pre>
-<p>Before pushing to GitHub, run the following commands:</p>
-<ol type="1">
-<li>Update conda environment: <code>make conda-env-update</code> or <code>uv venv</code> and <code>source .venv/bin/activate</code> followed by <code>uv sync --upgrade</code></li>
-<li>Install this package: <code>pip install -e .</code></li>
-<li>Sync with the latest <a href="https://github.com/ecmwf-projects/cookiecutter-conda-package">template</a> (optional): <code>make template-update</code></li>
-<li>Run quality assurance checks: <code>make qa</code></li>
-<li>Run tests: <code>make unit-tests</code></li>
-<li>Run the static type checker: <code>make type-check</code></li>
-<li>Build the documentation (see <a href="https://www.sphinx-doc.org/en/master/tutorial/">Sphinx tutorial</a>): <code>make docs-build</code></li>
-</ol>
-</section>
-<section id="license" class="level3">
-<h3 class="anchored" data-anchor-id="license">License</h3>
-<pre><code>Copyright 2023, OpenPTV consortium.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.</code></pre>
-</section>
-</section>
-</section>
-
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diff --git a/README.md b/README.md
index c2b7bbc..6466639 100644
--- a/README.md
+++ b/README.md
@@ -33,9 +33,11 @@ The project currently supports Python `>=3.12,<3.14`.
 
 ## Installation
 
-### Recommended: uv
+### Default user install
 
-Create the environment and install all project dependencies:
+#### Recommended: uv
+
+Create the environment and install the runtime dependencies:
 
 ```bash
 uv venv
@@ -43,25 +45,57 @@ source .venv/bin/activate
 uv sync
 ```
 
-This is the simplest way to get the current default stack, including NumPy,
-SciPy, Numba, and the `optv` native bindings when they are available for your
-platform and Python version.
+This gives you the standard runtime stack: NumPy, SciPy, Numba, and YAML
+support.
+
+If you also want native `optv` delegation when bindings are available for your
+platform and Python version, install the optional extra:
+
+```bash
+uv sync --extra native
+```
+
+#### Alternative: pip
+
+```bash
+conda create -n openptv-python -c conda-forge python=3.12
+conda activate openptv-python
+pip install .
+```
+
+Optional native bindings:
+
+```bash
+pip install ".[native]"
+```
+
+### Developer install
+
+#### Recommended: uv
+
+```bash
+uv venv
+source .venv/bin/activate
+uv sync --extra dev
+```
 
-### Alternative: conda + pip
+#### Alternative: conda + pip
 
 ```bash
 conda create -n openptv-python -c conda-forge python=3.12
 conda activate openptv-python
-pip install -e .
+pip install -e ".[dev]"
 ```
 
 ### What gets installed
 
-- `numba` is part of the default dependency set and accelerates selected Python
-  kernels automatically after the first call.
-- `optv` is part of the default dependency set and enables native interop on
-  supported platforms.
-- The public API stays the same regardless of which backend is active.
+- The default install contains the runtime dependencies only.
+- The optional `native` extra adds `optv` bindings for automatic native
+  delegation on supported platforms.
+- The optional `dev` extra adds test, docs, typing, and pre-commit tooling for
+  contributors.
+- The public API stays the same regardless of which backend extras are
+  installed.
 
 ## Backend Behavior
 
@@ -116,11 +150,15 @@ Use one of the installation methods above.
 uv run python - <<'PY'
 import openptv_python
 import numba
-import optv
+try:
+  import optv
+except ImportError:
+  print("optv not installed; native delegation disabled")
+else:
+  print("optv ok", optv.__version__)
 
 print("openptv_python ok")
 print("numba ok", numba.__version__)
-print("optv ok", optv.__version__)
 PY
 ```
 
@@ -153,10 +191,11 @@ conda create -n openptv-python -c conda-forge python=3.12
 conda activate openptv-python
 ```
 
-Before pushing to GitHub, run the following commands:
+Before pushing to GitHub, use the developer install above and then run the
+following commands:
 
-1. Update conda environment: `make conda-env-update` or `uv venv` and `source .venv/bin/activate` followed by `uv sync --upgrade`
-1. Install this package: `pip install -e .`
+1. Update conda environment: `make conda-env-update` or `uv venv` and `source .venv/bin/activate` followed by `uv sync --extra dev --upgrade`
+1. If you are using pip instead of uv, install the editable developer environment: `pip install -e ".[dev]"`
 1. Sync with the latest [template](https://github.com/ecmwf-projects/cookiecutter-conda-package) (optional): `make template-update`
 1. Run quality assurance checks: `make qa`
 1. Run tests: `make unit-tests`
diff --git a/ci/environment-ci.yml b/ci/environment-ci.yml
index b7136d7..7ea50bc 100644
--- a/ci/environment-ci.yml
+++ b/ci/environment-ci.yml
@@ -7,6 +7,8 @@ dependencies:
 - mypy
 - myst-parser
 - pip
+- pip:
+  - types-PyYAML
 - pre-commit
 - pydata-sphinx-theme
 - pytest
diff --git a/docs/index.md b/docs/index.md
index d414197..58315f4 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -5,7 +5,8 @@ Python version of the OpenPTV library.
 Start with the README for installation and backend selection details. The short
 version is:
 
-- install with `uv sync` on Python 3.12 or 3.13
+- install runtime dependencies with `uv sync` on Python 3.12 or 3.13
+- install contributor tooling with `uv sync --extra dev`
 - use the same Python API regardless of backend
 - get pure Python behavior everywhere, Numba acceleration in selected kernels,
   and automatic native `optv` delegation for preprocessing and full-frame
diff --git a/plans.md b/plans.md
deleted file mode 100644
index a7521ae..0000000
--- a/plans.md
+++ /dev/null
@@ -1,2 +0,0 @@
-openptv-python-2026-03-07-backend-design.md
-openptv-python-2026-03-07-pybind-delegation-map.md
diff --git a/pyproject.toml b/pyproject.toml
index 561046f..14aa1e2 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,32 +10,49 @@ classifiers = [
   "Operating System :: OS Independent",
   "Programming Language :: Python",
   "Programming Language :: Python :: 3",
-  "Programming Language :: Python :: 3.11",
   "Programming Language :: Python :: 3.12",
+  "Programming Language :: Python :: 3.13",
   "Topic :: Scientific/Engineering"
 ]
 dependencies = [
-  "mypy>=1.19.1",
-  "myst-parser>=5.0.0",
   "numba>=0.64.0",
   "numpy>=2.4.2",
+  "pyyaml>=6.0.3",
+  "scipy>=1.17.1"
+]
+description = "Python version of the OpenPTV library"
+dynamic = ["version"]
+license = {file = "LICENSE"}
+name = "openptv-python"
+readme = "README.md"
+requires-python = ">=3.12,<3.14"
+
+[project.optional-dependencies]
+docs = [
+  "myst-parser>=5.0.0",
+  "pydata-sphinx-theme>=0.16.1",
+  "sphinx>=9.1.0",
+  "sphinx-autoapi>=3.7.0"
+]
+native = [
+  "optv>=0.3.2"
+]
+test = [
+  "pytest>=9.0.2",
+  "pytest-cov>=7.0.0"
+]
+dev = [
+  "mypy>=1.19.1",
+  "myst-parser>=5.0.0",
   "optv>=0.3.2",
   "pre-commit>=4.5.1",
   "pydata-sphinx-theme>=0.16.1",
   "pytest>=9.0.2",
   "pytest-cov>=7.0.0",
-  "pyyaml>=6.0.3",
-  "scipy>=1.17.1",
   "sphinx>=9.1.0",
   "sphinx-autoapi>=3.7.0",
   "types-pyyaml>=6.0.12.20250915"
 ]
-description = "Python version of the OpenPTV library"
-dynamic = ["version"]
-license = {file = "LICENSE"}
-name = "openptv-python"
-readme = "README.md"
-requires-python = ">=3.12,<3.14"
 
 [tool.coverage.run]
 branch = true
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index 0b3e19c..10f575c 100644
--- a/uv.lock
+++ b/uv.lock
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From 200cca4eead6b59c3498f76628170b79a693a6e7 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sun, 15 Mar 2026 10:53:09 +0200
Subject: [PATCH 23/27] pre-commit also toml and lock

---
 pyproject.toml |   24 +-
 uv.lock        | 1186 ++++++++++++++++++++++++------------------------
 2 files changed, 605 insertions(+), 605 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 14aa1e2..1a8218b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -28,6 +28,18 @@ readme = "README.md"
 requires-python = ">=3.12,<3.14"
 
 [project.optional-dependencies]
+dev = [
+  "mypy>=1.19.1",
+  "myst-parser>=5.0.0",
+  "optv>=0.3.2",
+  "pre-commit>=4.5.1",
+  "pydata-sphinx-theme>=0.16.1",
+  "pytest>=9.0.2",
+  "pytest-cov>=7.0.0",
+  "sphinx>=9.1.0",
+  "sphinx-autoapi>=3.7.0",
+  "types-pyyaml>=6.0.12.20250915"
+]
 docs = [
   "myst-parser>=5.0.0",
   "pydata-sphinx-theme>=0.16.1",
@@ -41,18 +53,6 @@ test = [
   "pytest>=9.0.2",
   "pytest-cov>=7.0.0"
 ]
-dev = [
-  "mypy>=1.19.1",
-  "myst-parser>=5.0.0",
-  "optv>=0.3.2",
-  "pre-commit>=4.5.1",
-  "pydata-sphinx-theme>=0.16.1",
-  "pytest>=9.0.2",
-  "pytest-cov>=7.0.0",
-  "sphinx>=9.1.0",
-  "sphinx-autoapi>=3.7.0",
-  "types-pyyaml>=6.0.12.20250915"
-]
 
 [tool.coverage.run]
 branch = true
diff --git a/uv.lock b/uv.lock
index 10f575c..a96057f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,954 +1,954 @@
-version = 1
-revision = 3
 requires-python = ">=3.12, <3.14"
+revision = 3
+version = 1
 
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From b7b9d2c0b8ef164ef4f68324b64f8c1930c6316e Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sun, 15 Mar 2026 10:57:51 +0200
Subject: [PATCH 24/27] hooks

---
 .pre-commit-config.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 64c2474..a7a6829 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -33,6 +33,7 @@ repos:
     args: [--autofix, --preserve-quotes]
   - id: pretty-format-toml
     args: [--autofix]
+    exclude: ^uv\.lock$
 - repo: https://github.com/gitleaks/gitleaks
   rev: v8.30.0
   hooks:

From be368ec462036f8757402de2fa6746917721033b Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sun, 15 Mar 2026 10:59:18 +0200
Subject: [PATCH 25/27] fix hard coded path

---
 tests/test_tracking_ground_truth.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/tests/test_tracking_ground_truth.py b/tests/test_tracking_ground_truth.py
index 026954b..ec24e74 100644
--- a/tests/test_tracking_ground_truth.py
+++ b/tests/test_tracking_ground_truth.py
@@ -335,7 +335,9 @@ def _write_synthetic_tracking_fixture(
     add_distractor: bool = True,
 ) -> list[Calibration]:
     """Write a clean deterministic tracking fixture into a temporary workspace."""
-    source = Path("/home/user/Documents/GitHub/openptv-python/tests/testing_fodder")
+    source = Path(__file__).resolve().parent / "testing_fodder"
+    if not source.exists():
+        raise FileNotFoundError(f"Missing test fixture directory: {source}")
     shutil.copytree(source, workdir)
     os.chdir(workdir)
 

From 8ca86896a187b0be60a121642fb7cf58f961bd6f Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sun, 15 Mar 2026 10:59:43 +0200
Subject: [PATCH 26/27] Potential fix for pull request finding

Co-authored-by: Copilot Autofix powered by AI <175728472+Copilot@users.noreply.github.com>
---
 openptv_python/image_processing.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/openptv_python/image_processing.py b/openptv_python/image_processing.py
index 082e785..83156d2 100644
--- a/openptv_python/image_processing.py
+++ b/openptv_python/image_processing.py
@@ -132,7 +132,7 @@ def fast_box_blur(
         col = imx - filt_span
         left = row_start + imx - n
         right = left + 1
-        while col < row_start + imx - row_start and m > 0 and right < row_start + imx:
+        while col < imx and m > 0 and right < row_start + imx:
             accum -= int(src_flat[left]) + int(src_flat[right])
             row_accum[row_start + col] = accum * n // m
             left += 2

From 523efa72d60a9588e13e06267c3e65e1018e4771 Mon Sep 17 00:00:00 2001
From: Alex Liberzon <alexlib@users.noreply.github.com>
Date: Sun, 15 Mar 2026 12:34:52 +0200
Subject: [PATCH 27/27] mypy

---
 openptv_python/track.py             |  2 +-
 tests/test_tracking_ground_truth.py | 25 +++++++++++++++++++------
 2 files changed, 20 insertions(+), 7 deletions(-)

diff --git a/openptv_python/track.py b/openptv_python/track.py
index f9e2697..2a81377 100644
--- a/openptv_python/track.py
+++ b/openptv_python/track.py
@@ -253,7 +253,7 @@ def angle_acc(start: np.ndarray, pred: np.ndarray, cand: np.ndarray) -> np.ndarr
     acc = np.linalg.norm(v0 - v1)
 
     if np.all(v0 == -v1):
-        angle = 200
+        angle = 200.0
     elif np.all(v0 == v1):
         angle = 0
     else:
diff --git a/tests/test_tracking_ground_truth.py b/tests/test_tracking_ground_truth.py
index ec24e74..39784eb 100644
--- a/tests/test_tracking_ground_truth.py
+++ b/tests/test_tracking_ground_truth.py
@@ -7,6 +7,7 @@
 from contextlib import ExitStack, redirect_stdout
 from dataclasses import dataclass
 from pathlib import Path
+from typing import cast
 from unittest.mock import patch
 
 import numpy as np
@@ -186,7 +187,8 @@ def _make_bounds(*, dv_limit: float, dacc: float, dangle: float) -> dict[str, fl
 
 
 def _rounded_position(pos: np.ndarray) -> tuple[float, float, float]:
-    return tuple(round(float(value), 5) for value in pos)
+    values = tuple(round(float(value), 5) for value in pos)
+    return cast(tuple[float, float, float], values)
 
 
 def _snapshot_graph(snapshot):
@@ -359,7 +361,7 @@ def _write_synthetic_tracking_fixture(
 
     for frame_index, frame_num in enumerate(FRAMES):
         frame = Frame(3, 16)
-        cam_targets = [[] for _ in range(3)]
+        cam_targets: list[list[Target]] = [[] for _ in range(3)]
         correspond_indices = {
             track_id: [CORRES_NONE] * 4
             for track_id, positions in tracks.items()
@@ -427,7 +429,9 @@ def _write_synthetic_tracking_fixture(
                 correspond_indices[track_id],
                 dtype=np.int32,
             )
-            frame.path_info[row_index].x = tracks[track_id][frame_index]
+            position = tracks[track_id][frame_index]
+            assert position is not None
+            frame.path_info[row_index].x = position
 
         write_path_frame(
             frame.correspond,
@@ -446,7 +450,10 @@ def _snapshot_tracking_outputs() -> dict[
     int, list[tuple[int, int, tuple[int, int, int, int], tuple[float, float, float]]]
 ]:
     """Read the written tracking outputs into a stable frame-by-frame snapshot."""
-    snapshots = {}
+    snapshots: dict[
+        int,
+        list[tuple[int, int, tuple[int, int, int, int], tuple[float, float, float]]],
+    ] = {}
     for frame in FRAMES:
         correspond, path_info = read_path_frame(
             "res/particles",
@@ -458,8 +465,14 @@ def _snapshot_tracking_outputs() -> dict[
             (
                 int(path.prev_frame),
                 int(path.next_frame),
-                tuple(int(value) for value in correspond[idx].p),
-                tuple(round(float(value), 5) for value in path.x),
+                cast(
+                    tuple[int, int, int, int],
+                    tuple(int(value) for value in correspond[idx].p),
+                ),
+                cast(
+                    tuple[float, float, float],
+                    tuple(round(float(value), 5) for value in path.x),
+                ),
             )
             for idx, path in enumerate(path_info)
         ]