iskra ✨ Modern Geometry Processing

Lightweight geometry processing library that is a one-stop shop for all your geometric needs. Iskra is:

• modern and Python-first,
• simple by default, powerful when needed,
• fully differentiable and compatible with machine learning,
• CPU and GPU enabled,
• actievely maintained.

Support the project by starring it!

Caution

This is a pre-release. We are actively working on a user guide, documentation, and setting up on PyPI.

Example

Computing vertex normals in iskra is as simple as:

import torch

from iskra.geometry import triangle_normals
from iskra.mesh import Mesh
from iskra.topology import face_index, reduce_on_subface

mesh, _ = Mesh.from_path(
    "oded://objects/koala/koala_low_resolution.obj",
    device="cpu"
)
verts = mesh.vertices  # [V, 3]
faces = mesh.faces  # [F, 3]

tris = face_index(verts, faces)  # [F, 3, 3]

tri_normals = triangle_normals(tris)  # [F, 3]

vert_normals = reduce_on_subface(tri_normals, faces, verts.shape[0], "sum")  # [V, 3]
vert_normals = torch.nn.functional.normalize(vert_normals, dim=-1)  # [V, 3]

Obtaining iskra ✨

Change into the cloned iskra directory and install it to your active environment using:

pip install -e git+https://github.com/anadodik/iskra/

Development

Lastly, if you plan on contributing, you will need the development dependencies and to compile the C++ extensions in editable mode. This can be done by running the following:

conda env create -f environment.yaml
conda env update -f environment-dev.yaml
conda activate iskra
pip install --no-build-isolation -Ceditable.rebuild=true -ve .

FAQ

• Why the name?
  • Iskra means “spark” in Serbo-Croatian: a spark enables using (a) torch. We also expect our system to be the spark that ignites exciting research in geometry. Most importantly, it sounds cool.