Lidar and Point Cloud Processing

Downsample, denoise, transform, visualize, register, and fit geometrical shapes of 3-D point clouds

Point clouds are typically used to measure physical world surfaces. They have applications in robot navigation and perception, depth estimation, stereo vision, visual registration, and in advanced driver assistance systems (ADAS). Computer Vision Toolbox™ algorithms provide point cloud processing functionality for downsampling, denoising, and transforming point clouds. The toolbox also provides point cloud registration, geometrical shape fitting to 3-D point clouds, and the ability to read, write, store, display, and compare point clouds. You can also combine multiple point clouds to reconstruct a 3-D scene using the iterative closest point (ICP) algorithm.

Featured Examples