Overview

The Point Cloud Library (PCL) is a large scale, open project[1] for point cloud processing. The PCL framework contains numerous state-of-the art algorithms including filtering, feature estimation, surface reconstruction, registration, model fitting and segmentation. These algorithms can be used, for example, to filter outliers from noisy data, stitch 3D point clouds together, segment relevant parts of a scene, extract keypoints and compute descriptors to recognize objects in the world based on their geometric appearance, and create surfaces from point clouds and visualize them – to name a few.

PCL is released under the terms of the BSD license and is open source software. It is free for commercial and research use.

PCL is cross-platform, and has been successfully compiled and deployed on Linux, MacOS, Windows, and Android. To simplify development, PCL is split into a series of smaller code libraries, that can be compiled separately. This modularity is important for distributing PCL on platforms with reduced computational or size constraints.

Please visit http://www.pointclouds.org for more information.

Quick Links

• Main Website: http://www.pointclouds.org
• GitHub Repository: https://github.com/PointCloudLibrary
• Continuous Integration: https://dev.azure.com/PointCloudLibrary/pcl/_build
• Changelog: https://pointcloudlibrary.github.io/documentation/changelog.html

References

[1] For more information, including a scientific citation (more to be added soon), please see:

@InProceedings{Rusu_ICRA2011_PCL,
  author    = {Radu Bogdan Rusu and Steve Cousins},
  title     = {{3D is here: Point Cloud Library (PCL)}},
  booktitle = {{IEEE International Conference on Robotics and Automation (ICRA)}},
  month     = {May 9-13},
  year      = {2011},
  address   = {Shanghai, China},
  publisher = {IEEE}
}

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If you are referencing PCL in your work, please do contact us. We're trying to gather a list of publications that use PCL, and present it here. That will give your work visibility, and it would help us understand what parts of PCL should we be working on. Thank you.