Point Cloud Toolbox provides algorithms and apps for designing and analyzing point cloud processing systems. The toolbox provides functions for registration, filtering, geometric transformation, clustering, and segmentation of point clouds. It supports processing point cloud data from sensors such as lidar, RGB-D cameras, stereo cameras, and mmWave radar, and from workflows such as visual SLAM, photogrammetry, and structure-from-motion.
You can read data from standard formats or stream it directly from lidar sensors. Visualization tools support analysis, measurement, and interactive editing of large point clouds.
Apps enable lidar-camera calibration, multi-sensor labeling, and registration. You can apply deep learning techniques for the detection, segmentation, and classification of point clouds. You can also generate C/C++ (with MATLAB Coder) and CUDA® codes (with GPU Coder) for deployment.
Read, Write, and Visualize Point Clouds
Import point clouds from file formats such as LAS/LAZ, PCD, and PLY, or stream point cloud data directly from lidar sensors, including Velodyne® and Ouster®. Visualize and explore point cloud data across datasets of any size using the Point Cloud Analyzer app.
Process and Analyze Point Clouds
Process and analyze ground-based and aerial point clouds using algorithms for unorganized-to-organized conversion, ground segmentation, clustering, shape fitting, geometric transformation, and feature extraction.
Point Cloud Registration and SLAM
Register point clouds using algorithms such as ICP, NDT, and FPFH-based feature matching. Evaluate and compare registration algorithms using the Point Cloud Registration Analyzer app. Implement 3D SLAM workflows for mapping and localization for ground-based and aerial point clouds.
Lidar Camera Calibration
Estimate rigid transformation between a 3D lidar sensor and a camera. Use the estimated rigid transformation matrix between camera and lidar to fuse the data from these two sensors.
Deep Learning for Point Clouds
Apply deep learning algorithms for semantic segmentation and object detection on point clouds. Train, test, and evaluate performance of these networks. Deploy them on target hardware by generating C/C++ or CUDA code.
Point Cloud and Video Labeling
Label point clouds and videos for object detection, semantic segmentation, and lane detection. Use the Multi-Sensor Labeler app with built-in or custom automation algorithms to accelerate the labeling process.
Product Resources:
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