Skip to main content

Marching cubes on sparse matrices

Project description


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.


pip3 install git+


>>> 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


  • 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.
  • Currently only full edges.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sparse-cubes-0.1.0.tar.gz (18.2 kB view hashes)

Uploaded source

Built Distribution

sparse_cubes-0.1.0-py3-none-any.whl (17.2 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page