IEEE/CVF International Conference on Computer Vision (ICCV), 2023
Authors:
Valter Piedade (Instituto Superior Tecnico, Lisboa)
Pedro Miraldo (Mitsubishi Electric Research)
Aug 21, 2023
Summary:
Implementation of BANSAC, a new guided sampling process for RANSAC. Previous methods either assume no prior information about the inlier/outlier classification of data points or use some previously computed scores in the sampling. We derive a dynamic Bayesian network that updates individual data points’ inlier scores while iterating RANSAC. At each iteration, we apply weighted sampling using the updated scores. Our method works with or without prior data point scorings. In addition, we use the updated inlier/outlier scoring for deriving a new stopping criterion for the RANSAC loop. We test our method using three real-world datasets in different applications and obtain state-of-the-art results. Our method outperforms the baselines in accuracy while needing less computational time.
Tested in Ubuntu 22.04, Python 3.10, and OpenCV 4.6.0 (docker image ubuntu:jammy).
Requirements:
- Anaconda - use instructions https://docs.anaconda.com/anaconda/install/index.html
- Eigen3, cmake, wget, zip, git and build-essential:
apt install libeigen3-dev cmake wget zip git build-essential
Download the repository and thirdparties
git clone <path_to_repository>
cd bansac
git submodule update --init --recursive
Configure virtual environment
conda env create -f environment.yaml
conda activate bansac
Clone and compile Opencv, changing the calib3d module to our modified version. Change <path_to_anaconda> to the path to the anaconda folder.
cd opencv
git apply ../bansac.patch
mkdir build && cd build
mkdir install
cmake -D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=install \
-D PYTHON3_LIBRARY=${path_to_anaconda}/envs/bansac/lib \
-D PYTHON3_INCLUDE_DIR=${path_to_anaconda}/envs/bansac/include/python3.10 \
-D PYTHON3_EXECUTABLE=${path_to_anaconda}/envs/bansac/bin/python3.10 \
-D PYTHON3_PACKAGES_PATH=${path_to_anaconda}/envs/bansac/lib/python3.10/site-packages \
-D BUILD_opencv_python2=OFF \
-D BUILD_opencv_python3=ON \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D INSTALL_C_EXAMPLES=OFF \
-D OPENCV_ENABLE_NONFREE=ON \
-D BUILD_EXAMPLES=OFF \
-D BUILD_NEW_PYTHON_SUPPORT=ON \
-D HAVE_opencv_python3=ON \
-D PYTHON_DEFAULT_EXECUTABLE=${path_to_anaconda}/envs/bansac/bin/python3.10 ..
make -j${nproc}
make install
cd ../../
For essential and fundamental matrices estimation:
mkdir data && cd data
wget http://cmp.felk.cvut.cz/~mishkdmy/CVPR-RANSAC-Tutorial-2020/RANSAC-Tutorial-Data-ValOnly.tar
wget http://cmp.felk.cvut.cz/~mishkdmy/CVPR-RANSAC-Tutorial-2020/RANSAC-Tutorial-Data-EF.tar
tar -xf RANSAC-Tutorial-Data-ValOnly.tar
tar -xf RANSAC-Tutorial-Data-EF.tar
ln -s RANSAC-Tutorial-Data-ValOnly/val/* ./
ln -s RANSAC-Tutorial-Data/train/* ./
cd ../
For homography estimation:
cd data
wget http://cmp.felk.cvut.cz/~mishkdmy/CVPR-RANSAC-Tutorial-2020/homography.tar.gz
tar -xf homography.tar.gz
ln -s homography/* ./
cd ../
Essential and Fundamental matrices estimation, pantheon_exterior sequence:
python3 evaluation_relative_pose_script.py --problem=essential --number_pairs=4000 --sequence=pantheon_exterior
python3 evaluation_relative_pose_script.py --problem=fundamental --number_pairs=4000 --sequence=pantheon_exterior
For homography matrix estimation, HPatches sequence:
python3 evaluation_homography_script.py --sequence=HPatches
If you use the software, please cite the following:
@inproceedings{piedade2023,
author = {Valter Piedade and Pedro Miraldo},
title = {BANSAC: A dynamic BAyesian Network for adaptive SAmple Consensus},
booktitle = {IEEE/CVF International Conference on Computer Vision (ICCV)},
year = 2023,
}Pedro Miraldo
Principal Research Scientist
Mitsubishi Electric Research Laboratories
E-Mail: miraldo@merl.com
See CONTRIBUTING.md for our policy on contributions.
Released under AGPL-3.0-or-later license, as found in the LICENSE.md file.
All files, except as noted below:
Copyright (C) 2023 Mitsubishi Electric Research Laboratories (MERL).
SPDX-License-Identifier: AGPL-3.0-or-later
Before patching, OpenCV 4.6.0 is taken without modifications from https://github.com/opencv/opencv (license included in LICENSES/Apache-2.0.txt):
Copyright (C) 2000-2022, Intel Corporation, all rights reserved.
Copyright (C) 2009-2011, Willow Garage Inc., all rights reserved.
Copyright (C) 2009-2016, NVIDIA Corporation, all rights reserved.
Copyright (C) 2010-2013, Advanced Micro Devices, Inc., all rights reserved.
Copyright (C) 2015-2022, OpenCV Foundation, all rights reserved.
Copyright (C) 2008-2016, Itseez Inc., all rights reserved.
Copyright (C) 2019-2022, Xperience AI, all rights reserved.
Copyright (C) 2019-2022, Shenzhen Institute of Artificial Intelligence and
Robotics for Society, all rights reserved.
The following file:
utils.py
was taken without modification from https://github.com/ducha-aiki/ransac-tutorial-2020-data (license included in LICENSES/Apache-2.0.txt):
Copyright (C) 2020 Google LLC, University of Victoria, Czech Technical University
The datasets were taken without modification from http://cmp.felk.cvut.cz/~mishkdmy/CVPR-RANSAC-Tutorial-2020/. The three files RANSAC-Tutorial-Data-ValOnly.tar, RANSAC-Tutorial-Data-EF.tar, and homography.tar.gz provide three datasets: HPatches, EVD, and Phototourism:
- The HPatches dataset has license included in LICENSES/MIT.txt:
Copyright (C) 2020 HPatches
-
The EVD dataset for homography matrix estimation contains 11 images with license LICENSES/Apache-2.0.txt): GRAND, PKK, FACE, GIRL, SHOP, DUM, INDEX, CAFE, FOX, CAT, and VIN. Images THERE, GRAF, ADAM, and MAG are from unknown sources.
-
The Phototourism dataset contain images popular landmarks originally collected by the Yahoo Flickr Creative Commons 100M under Creative Commons licenses.