Point Cloud Library

The Point Cloud Library (PCL) is an <a href="/facts/Open-source_software/HzTaB8E9">open-source</a> <a href="/facts/Software_library/xRzL3P7q">library</a> of algorithms for <a href="/facts/Point_cloud/dN8eG4AU">point cloud</a> processing tasks and 3D <a href="/facts/Geometry_processing/Hq3So4MP">geometry processing</a>, such as occur in three-dimensional <a href="/facts/Computer_vision/Tl2Yyk66">computer vision</a>. The library contains algorithms for filtering, feature estimation, surface reconstruction, <a href="/facts/Point_set_registration/EevAmm50">3D registration</a>, <a href="/facts/Model_fitting/zrLKw6Xj">model fitting</a>, <a href="/facts/Object_Recognition/cdLpak1I">object recognition</a>, and <a href="/facts/Image_segmentation/jofAhbxa">segmentation</a>. Each module is implemented as a smaller library that can be compiled separately (for example, libpcl_filters, libpcl_features, libpcl_surface, ...). PCL has its own data format for storing point clouds - PCD (Point Cloud Data), but also allows datasets to be loaded and saved in many other formats. It is written in <a href="/facts/C%252B%252B/Et0F2qn9">C++</a> and released under the <a href="/facts/BSD_license/XLtmLp5x">BSD license</a>.
These algorithms have been used, for example, for <a href="/facts/Perception/xLK0n2q4">perception</a> in <a href="/facts/Robotics/wkmvodJz">robotics</a> to filter outliers from noisy data, <a href="/facts/Point_set_registration/EevAmm50">stitch 3D point clouds together</a>, 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.
PCL requires several <a href="/facts/Third-party_software_component/NFlWUV95">third-party</a> libraries to function, which must be installed. Most mathematical operations are implemented using the <a href="/facts/Eigen_(C%252B%252B_library)/js8e67k3">Eigen</a> library. The visualization module for 3D point clouds is based on <a href="/facts/VTK/SXTm1vTl">VTK</a>. <a href="/facts/Boost_(C%252B%252B_libraries)/vGLGG51A">Boost</a> is used for shared pointers and the FLANN library for quick k-nearest neighbor search. Additional libraries such as Qhull, <a href="/facts/OpenNI/WPobqTko">OpenNI</a>, or <a href="/facts/Qt_(software)/gVhwlQnJ">Qt</a> are optional and extend PCL with additional features.
PCL is <a href="/facts/Cross-platform_software/16zqdvNR">cross-platform software</a> that runs on the most commonly used <a href="/facts/Operating_system/8XTuvMh2">operating systems</a>: <a href="/facts/Linux/9FvCKSTq">Linux</a>, <a href="/facts/Microsoft_Windows/bwhAhuSL">Windows</a>, <a href="/facts/MacOS/Y93vVpTm">macOS</a> and <a href="/facts/Android_(operating_system)/nNyHb0xz">Android</a>. The library is fully integrated with the <a href="/facts/Robot_Operating_System/vWQLMAbT">Robot Operating System</a> (ROS) and provides support for <a href="/facts/OpenMP/69IpI80o">OpenMP</a> and <a href="/facts/Intel/SMF0gJJX">Intel</a> <a href="/facts/Threading_Building_Blocks/6DChhaHg">Threading Building Blocks</a> (TBB) libraries for multi-core <a href="/facts/Parallel_computing/nQzcpzQt">parallelism</a>.
The library is constantly updated and expanded, and its use in various industries is constantly growing. For example, PCL participated in the <a href="/facts/Google_Summer_of_Code/M8rDIn6W">Google Summer of Code</a> 2020 initiative with three projects. One was the extension of PCL for use with <a href="/facts/Python_(programming_language)/YbuGqofa">Python</a> using Pybind11.
A large number of examples and tutorials are available on the PCL website, either as C++ source files or as tutorials with a detailed description and explanation of the individual steps.

Point Cloud Library open-in-new

Point Cloud Library