# PointCloudLibrary/pcl

Point Cloud Library (PCL)

Repository: https://github.com/PointCloudLibrary/pcl
Canonical: https://ross.abutalabs.com/products/pcl
Homepage: https://pointclouds.org/
Language: C++
License: NOASSERTION
License Family: other
Topics: pcl, c-plus-plus, cpp, pointcloud, computer-vision, point-cloud
Last push: 2026-08-18T08:22:12+00:00

## Health v2 (maintenance only)
Score: 68/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 12, longevity 100
- inputs: {"age_days": 4950, "days_push": 15, "days_rel": 372, "gap_med": 185, "n_releases_24m": 2}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 11101, forks 4697 (observed 2026-08-28T04:10:46.101105+00:00)

## What it is
The Point Cloud Library (PCL) is a large-scale, modular open-source C++ library for 2D/3D image and point cloud processing. It provides state-of-the-art algorithms for filtering, feature estimation, registration, segmentation, surface reconstruction, object recognition, and visualization, released under the BSD license.

## Use cases
- filter outliers from noisy 3D point cloud data
- stitch or register multiple 3D point clouds together
- segment objects out of a 3D scene from a depth camera or lidar
- extract keypoints and compute descriptors to recognize objects by geometry
- reconstruct a 3D surface or mesh from a point cloud
- visualize and perform I/O on point cloud data from 3D scanners

## When to choose
- you need a comprehensive, battle-tested C++ toolkit for point cloud processing
- you work with 3D sensors like lidar, stereo cameras, or time-of-flight cameras
- you need modular libraries covering the full pipeline from I/O to recognition
- you need a permissively licensed (BSD) library usable in commercial products

## When to avoid
- you need a lightweight Python-first point cloud workflow (consider Open3D instead)
- your project is not in C++ or lacks the ability to build large native dependencies
- you only need simple mesh processing without point cloud algorithms
- you need GPU-accelerated real-time processing out of the box without extra setup

## Facets
- artifact type: library
- maturity: stable
- function: image-processing, computer-vision, graphics, data-visualization, file-system, math
- domain: computer-vision, robotics, graphics
- platform: cpp, windows, cross-platform
- tags: point-cloud, 3d-processing, lidar, depth-cameras, 3d-scanning, registration, segmentation, surface-reconstruction, algorithms, linux, macos, android

## Member repositories
- PointCloudLibrary/pcl (main) score 68

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:46.101105+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 2026-08-29T17:16:48.083084+00:00, confidence not recorded.
  - readme: https://github.com/PointCloudLibrary/pcl (fetched 2026-08-28T04:10:46.101105+00:00, sha 0a8eac7ca84e)
  - homepage: https://pointclouds.org/ (fetched 2026-08-29T08:15:26.272541+00:00, sha 0b1a42fdb08c)
  - site_page: https://pointclouds.org/about (fetched 2026-08-29T08:15:26.282126+00:00, sha a46967804b9d)
- Data as of 2026-08-30T08:39:29.467469+00:00.