sparse-cubes

Marching cubes for sparse matrices - i.e. (N, 3) voxel data.

Running marching cubes directly on sparse voxels is faster and importantly much more memory efficient than converting to a 3d matrix and using the implementation in e.g. sklearn.

The only dependencies are numpy and trimesh. Will use fastremap if present.

Install

pip3 install git+https://github.com/navis-org/sparse-cubes.git

Usage

>>> import sparsecubes as sc
>>> import numpy as np
>>> voxels = np.array([[0, 0, 0], [0, 0, 1]])
>>> m = sc.marching_cubes(voxels)
>>> m
<trimesh.Trimesh(vertices.shape=(12, 3), faces.shape=(20, 3))>
>>> m.is_winding_consistent
True

Notes

• Currently the mesh might have non-manifold edges. Trimesh will report these meshes as not watertight but in the very literal definition they do hold water.