fwilliams/point-cloud-utils

Health v2 · maintenance only

Adoption not part of the score

1549 stars · 119 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Point Cloud Utils (pcu) is an easy-to-use Python library for processing and manipulating 3D point clouds and triangle meshes, built on C++ backends like nanoflann and embree. It provides mesh I/O, point sampling, distance metrics (Chamfer, Hausdorff, Sinkhorn), signed distance computation, and mesh repair utilities via NumPy arrays.

Use cases

• sample points on a mesh with poisson disk sampling
• compute chamfer distance between two point clouds
• make a mesh watertight
• compute signed distances from points to a mesh
• load and save meshes in ply obj stl formats
• downsample a point cloud on a voxel grid
• estimate normals from a point cloud
• compute hausdorff distance between point clouds

When to choose

• you need common 3D geometry operations in Python with a simple NumPy-based API
• you want fast k-nearest-neighbor, ray-mesh intersection, or distance metrics without writing C++
• you need mesh repair, decimation, or watertighting utilities
• you are evaluating generative 3D models and need Chamfer/Wasserstein metrics

When to avoid

• you need interactive 3D visualization or a GUI
• you require GPU-accelerated batch processing at deep-learning scale
• you need full-featured mesh editing like a DCC tool (Blender, MeshLab)

Facets

library · maturity active

image-processing math parser data-science computer-vision graphics machine-learning data-science python cross-platform point-cloud mesh-processing 3d-geometry chamfer-distance hausdorff-distance poisson-disk-sampling numpy sinkhorn watertight-mesh nearest-neighbors algorithms

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