* [ cilantro ](https://github.com/kzampog/cilantro) \- A lean C++ library for working with point cloud data. 
  * [ open3d ](https://github.com/intel-isl/Open3D) \- Open3D: A Modern Library for 3D Data Processing. 
  * [ SqueezeSeg ](https://github.com/BichenWuUCB/SqueezeSeg) \- Implementation of SqueezeSeg, convolutional neural networks for LiDAR point clout segmentation. 
  * [ point_cloud_io ](https://github.com/ANYbotics/point_cloud_io) \- ROS nodes to read and write point clouds from and to files (e.g. ply, vtk). 
  * [ python-pcl ](https://github.com/strawlab/python-pcl) \- Python bindings to the pointcloud library. 
  * [ libpointmatcher ](https://github.com/ethz-asl/libpointmatcher) \- An "Iterative Closest Point" library for 2-D/3-D mapping in Robotics. 
  * [ depth_clustering ](https://github.com/PRBonn/depth_clustering) \- Fast and robust clustering of point clouds generated with a Velodyne sensor. 
  * [ lidar-bonnetal ](https://github.com/PRBonn/lidar-bonnetal) \- Semantic and Instance Segmentation of LiDAR point clouds for autonomous driving. 
  * [ CSF ](https://github.com/jianboqi/CSF) \- LiDAR point cloud ground filtering / segmentation (bare earth extraction) method based on cloth simulation. 
  * [ robot_body_filter ](https://github.com/peci1/robot_body_filter) \- A highly configurable LaserScan/PointCloud2 filter that allows to dynamically remove the 3D body of the robot from the measurements. 
  * [ grid_map ](https://github.com/ANYbotics/grid_map) \- Universal grid map library for mobile robotic mapping. 
  * [ elevation_mapping ](https://github.com/ANYbotics/elevation_mapping) \- Robot-centric elevation mapping for rough terrain navigation. 
  * [ rangenet_lib ](https://github.com/PRBonn/rangenet_lib) \- Contains simple usage explanations of how the RangeNet++ inference works with the TensorRT and C++ interface. 
  * [ pointcloud_to_laserscan ](https://github.com/ros-perception/pointcloud_to_laserscan) \- Converts a 3D Point Cloud into a 2D laser scan. 
  * [ octomap ](https://github.com/OctoMap/octomap) \- An Efficient Probabilistic 3D Mapping Framework Based on Octrees. 
  * [ pptk ](https://github.com/heremaps/pptk) \- Point Processing Toolkit from HEREMaps. 
  * [ gpu-voxels ](https://www.gpu-voxels.org/) \- GPU-Voxels is a CUDA based library which allows high resolution volumetric collision detection between animated 3D models and live pointclouds from 3D sensors of all kinds. 
  * [ spatio_temporal_voxel_layer ](https://github.com/SteveMacenski/spatio_temporal_voxel_layer) \- A new voxel layer leveraging modern 3D graphics tools to modernize navigation environmental representations. 
  * [ LAStools ](https://github.com/LAStools/LAStools) \- Award-winning software for efficient LiDAR processing. 
  * [ PCDet ](https://github.com/sshaoshuai/PCDet) \- A general PyTorch-based codebase for 3D object detection from point cloud. 
  * [ PDAL ](https://github.com/PDAL/PDAL) \- A C++ BSD library for translating and manipulating point cloud data. 
  * [ PotreeConverter ](https://github.com/potree/PotreeConverter) \- Builds a potree octree from las, laz, binary ply, xyz or ptx files. 
  * [ fast_gicp ](https://github.com/SMRT-AIST/fast_gicp) \- A collection of GICP-based fast point cloud registration algorithms. 
  * [ ndt_omp ](https://github.com/koide3/ndt_omp) \- Multi-threaded and SSE friendly NDT algorithm. 
  * [ laser_line_extraction ](https://github.com/kam3k/laser_line_extraction) \- A ROS packages that extracts line segments from LaserScan messages. 
  * [ Go-ICP ](https://github.com/yangjiaolong/Go-ICP) \- Implementation of the Go-ICP algorithm for globally optimal 3D pointset registration. 
  * [ PointCNN ](https://github.com/yangyanli/PointCNN) \- A simple and general framework for feature learning from point clouds. 
  * [ segmenters_lib ](https://github.com/LidarPerception/segmenters_lib) \- The LiDAR segmenters library, for segmentation-based detection. 
  * [ MotionNet ](https://github.com/pxiangwu/MotionNet) \- Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps. 
  * [ PolarSeg ](https://github.com/edwardzhou130/PolarSeg) \- An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. 
  * [ traversability_mapping ](https://github.com/TixiaoShan/traversability_mapping) \- Takes in point cloud from a Velodyne VLP-16 Lidar and outputs a traversability map for autonomous navigation in real-time. 
  * [ lidar_super_resolution ](https://github.com/RobustFieldAutonomyLab/lidar_super_resolution) \- Simulation-based Lidar Super-resolution for Ground Vehicles. 
  * [ Cupoch ](https://github.com/neka-nat/cupoch) \- A library that implements rapid 3D data processing and robotics computation using CUDA. 
  * [ linefit_ground_segmentation ](https://github.com/lorenwel/linefit_ground_segmentation) \- Implementation of the ground segmentation algorithm. 
  * [ Draco ](https://github.com/google/draco) \- A library for compressing and decompressing 3D geometric meshes and point clouds. 
  * [ Votenet ](https://github.com/facebookresearch/votenet) \- Deep Hough Voting for 3D Object Detection in Point Clouds. 
  * [ lidar_undistortion ](https://github.com/ethz-asl/lidar_undistortion) \- Provides lidar motion undistortion based on an external 6DoF pose estimation input. 
  * [ superpoint_graph ](https://github.com/loicland/superpoint_graph) \- Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs. 
  * [ RandLA-Net ](https://github.com/QingyongHu/RandLA-Net) \- Efficient Semantic Segmentation of Large-Scale Point Clouds. 
  * [ Det3D ](https://github.com/poodarchu/Det3D) \- A first 3D Object Detection toolbox which provides off the box implementations of many 3D object detection algorithms such as PointPillars, SECOND, PIXOR.