pointcloudlibrary/pcl

Point Cloud Library (PCL)

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Summary Information

Updated 23 minutes ago
Added to GitGenius on September 5th, 2026
Created on February 12th, 2013
Open Issues & Pull Requests: 581 (+1)
GitHub issues: Enabled
Number of forks: 4,700
Total Stargazers: 11,109 (+0)
Total Subscribers: 410 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 5.4 hours
Mean response time: 27.1 days
90th percentile: 6.0 days
Tracked items: 192

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 85% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 6% of issues opened in the past year have been closed. Three people close 77% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 40
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 906 days
Stale 30+ days: 37
Stale 90+ days: 36

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • kind: bug (97)
  • status: triage (44)
  • kind: compile error (28)
  • kind: question (24)
  • kind: request (22)
  • module: cmake (15)
  • help wanted (14)
  • module: registration (12)

Most active issues this week

No issue events were indexed in the last 7 days.

Detailed Description

Point Cloud Library (PCL) is a C++ library for processing and analyzing three-dimensional point cloud data in computer vision and robotics applications.

PCL addresses the challenge of working with unstructured 3D point cloud data by providing a comprehensive set of algorithms and data structures for filtering, feature estimation, surface reconstruction, registration, and segmentation. The library abstracts away low-level implementation details, allowing developers to build point cloud processing pipelines by composing modular components that handle common operations on 3D geometric data.

Developers should adopt PCL if they are building applications that consume data from depth sensors, LiDAR scanners, or other 3D acquisition devices and need to perform geometric analysis or transformation on that data. The library suits robotics projects, autonomous systems, 3D scanning applications, and computer vision systems that operate on point clouds. PCL is particularly valuable when you need a mature, well-tested implementation of standard algorithms rather than building these from scratch.

The project maintains active continuous integration across multiple platforms including Ubuntu, Windows, and macOS with both GCC and Clang compilers, ensuring builds remain stable across different environments. Documentation is actively maintained and published through multiple channels including the project website and Read the Docs. The community remains engaged through Discord, Stack Overflow, and the project website, providing channels for users to seek help and contribute.