# fwilliams/point-cloud-utils

An easy-to-use Python library for processing and manipulating 3D point clouds and meshes.

Repository: https://github.com/fwilliams/point-cloud-utils
Canonical: https://ross.abutalabs.com/products/point-cloud-utils
Homepage: https://www.fwilliams.info/point-cloud-utils/
Language: C++
License: MIT
License Family: permissive
Topics: poisson-disk-sampling, poisson, sampling, mesh, geometry, python, numpy, lloyd-relaxation, hausdorff-measure, hausdorff, hausdorff-distance, poisson-disc-sampling, nearest-neighbor, nanoflann, optimal-transport, sinkhorn, sinkhorn-distance, chamfer-distance, point-cloud, nearest-neighbors
Last push: 2025-09-10T00:24:36+00:00

## Health v2 (maintenance only)
Score: 52/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 41, release rhythm 40, longevity 100
- inputs: {"age_days": 2889, "days_push": 358, "days_rel": 505, "gap_med": 12.0, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1549, forks 119 (observed 2026-08-28T04:05:01.967839+00:00)

## What it is
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
- artifact type: library
- maturity: active
- function: image-processing, math, parser, data-science
- domain: computer-vision, graphics, machine-learning, data-science
- platform: python, cross-platform
- tags: point-cloud, mesh-processing, 3d-geometry, chamfer-distance, hausdorff-distance, poisson-disk-sampling, numpy, sinkhorn, watertight-mesh, nearest-neighbors, algorithms

## Member repositories
- fwilliams/point-cloud-utils (main) score 52

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:01.967839+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:30:22.779645+00:00, confidence not recorded.
  - readme: https://github.com/fwilliams/point-cloud-utils (fetched 2026-08-28T04:05:01.967839+00:00, sha 696ea1ec3e9f)
  - homepage: https://www.fwilliams.info/point-cloud-utils/ (fetched 2026-08-29T11:31:03.701204+00:00, sha c6cc34cfb184)
- Data as of 2026-08-30T08:39:29.467469+00:00.