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Preprocess, visualize, register, fit geometrical shapes, build maps, implement
SLAM algorithms, and use deep learning with 3-D point clouds

A point cloud is a set of data points in 3-D space. The points together represent
a 3-D shape or object. Each point in the data set is represented by an
*x*, *y*, and *z* geometric
coordinate. Point clouds provide a means of assembling a large number of single
spatial measurements into a dataset that can be represented as a describable object.
Point cloud processing is used 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.

You can use `pcregistericp`

, `pcregisterndt`

, `pcregistercorr`

, and `pcregistercpd`

to register a moving point cloud to a fixed point
cloud. These registration algorithms are based on the Iterative Closest Point (ICP)
algorithm, the Normal-Distributions Transform (NDT) algorithm, the phase correlation
algorithm, and the Coherent Point Drift (CPD) algorithm, respectively. You can build
a map with the registered point clouds, detect loop closures, optimize the map to
correct for drift, and perform localization in the prebuilt map. For more details,
see Point Cloud SLAM Overview.

The Stanford Triangle Format

Understand point cloud registration and mapping workflow.

**Getting Started with Point Clouds Using Deep Learning**

Understand how to use point clouds for deep learning.

**Choose Function to Visualize Detected Objects**

Compare visualization functions.

**Labeling, Segmentation, and Detection (Lidar Toolbox)**

Label, segment, detect, and track objects in point cloud data using deep learning and geometric algorithms