# daavoo/pyntcloud

pyntcloud is a Python library for working with 3D point clouds.

Repository: https://github.com/daavoo/pyntcloud
Canonical: https://ross.abutalabs.com/products/pyntcloud
Homepage: http://pyntcloud.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: point-clouds, python, 3d-point-clouds, 3d-graphics, 3d-models, 3d, python-library, deep-learning, 3d-deep-learning
Last push: 2026-07-10T08:46:17+00:00

## Health v2 (maintenance only)
Score: 64/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 91, release rhythm 8, longevity 100
- inputs: {"age_days": 3621, "days_push": 54, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1500, forks 230 (observed 2026-08-28T04:04:54.164281+00:00)

## What it is
pyntcloud is a Python library for working with 3D point clouds, built on the Python scientific stack (pandas, numpy). It supports loading/saving point cloud formats, scalar field computation, voxelization, sampling, and conversion to/from libraries like Open3D and PyVista.

## Use cases
- load and save 3D point clouds in PLY or NPZ format
- voxelize a point cloud and downsample it
- convert point clouds between Open3D and PyVista
- compute scalar fields like HSV from RGB point data
- process 3D LiDAR scan data in Python
- prepare point clouds for deep learning pipelines

## When to choose
- you need lightweight point cloud I/O and processing in Python
- you want easy interop with Open3D or PyVista
- you prefer working with pandas/numpy data structures for 3D data

## When to avoid
- you need full-featured 3D geometry processing like mesh repair or registration
- you need GPU-accelerated point cloud processing
- you work outside the Python ecosystem

## Facets
- artifact type: library
- maturity: active
- function: image-processing, data-science, graphics, file-system
- domain: computer-vision, graphics, data-science, machine-learning
- platform: python, cross-platform
- tags: point-clouds, 3d, voxel-grid, ply, open3d, pyvista, numpy

## Member repositories
- daavoo/pyntcloud (main) score 64

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:54.164281+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 2026-08-30T04:32:59.607493+00:00, confidence not recorded.
  - readme: https://github.com/daavoo/pyntcloud (fetched 2026-08-28T04:04:54.164281+00:00, sha 162f0c6a9f1d)
  - registry_pypi: https://pypi.org/pypi/pyntcloud/json (fetched 2026-08-29T11:38:27.687192+00:00, sha 712f6e657b3f)
- Data as of 2026-08-30T08:39:29.467469+00:00.