Segmentation, Detection, and Labeling

Segment, detect, label, and track objects in point cloud data using deep learning and geometric algorithms

Lidar Toolbox™ includes geometric and pretrained deep learning algorithms to segment point cloud data as well as detect and track objects of interest. You can apply the deep learning algorithms in advanced driver assistance systems (ADAS) applications to segment and detect vehicles.

Featured Examples

Detect, Classify, and Track Vehicles Using Lidar

Detect, Classify, and Track Vehicles Using Lidar

Detect, classify, and track vehicles by using lidar point cloud data captured by a lidar sensor mounted on an ego vehicle. The lidar data used in this example is recorded from a highway-driving scenario. In this example, the point cloud data is segmented to determine the class of objects using the PointSeg network. A joint probabilistic data association (JPDA) tracker with an interactive multiple model filter is used to track the detected vehicles.

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Lidar Point Cloud Semantic Segmentation Using PointSeg Deep Learning Network

Lidar Point Cloud Semantic Segmentation Using PointSeg Deep Learning Network

Train a PointSeg semantic segmentation network on 3-D organized lidar point cloud data.

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Lidar Point Cloud Semantic Segmentation Using SqueezeSegV2 Deep Learning Network

Lidar Point Cloud Semantic Segmentation Using SqueezeSegV2 Deep Learning Network

Train a SqueezeSegV2 semantic segmentation network on 3-D organized lidar point cloud data.

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Code Generation for Lidar Point Cloud Segmentation Network

Code Generation for Lidar Point Cloud Segmentation Network

Generate CUDA® MEX code for a deep learning network for lidar semantic segmentation. This example uses a pretrained SqueezeSegV2 [1] network that can segment organized lidar point clouds belonging to three classes (background, car, and truck). For information on the training procedure for the network, see Lidar Point Cloud Segmentation Using SqueezeSegV2 Deep Learning Network. The generated MEX code takes a point cloud as input and performs prediction on the point cloud by using the DAGNetwork object for the SqueezeSegV2 network.

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Lidar 3-D Object Detection Using PointPillars Deep Learning

Lidar 3-D Object Detection Using PointPillars Deep Learning

Train a PointPillars network for object detection in point clouds.

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Lidar Toolbox Documentation

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