Labeling, Segmentation, and Detection

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

Lidar Toolbox™ includes geometric and pre-trained 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. You can use the Lidar Labeler app to prepare labeled training data for the deep learning algorithms.

Highlighted Topics

Labeling
Interactive ground truth labeling in lidar point clouds
Segmentation
Segment point cloud data using deep learning and geometric algorithms
Detection and Tracking
Object detection, shape fitting, and tracking in lidar point cloud data

Featured Examples

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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Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning

Train a PointNet++ deep learning network to perform semantic segmentation on aerial lidar data.

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Terrain Classification for Aerial Lidar Data

Segment and classify terrain in aerial lidar data as ground, building, and vegetation. The example uses a LAZ file captured by an airborne lidar system as input. First, classify the point cloud data in the LAZ file into ground and non-ground points. Then, further classify non-ground points into building and vegetation points based on normals and curvature features. This figure provides an overview of the process.

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Lidar 3-D Object Detection Using PointPillars Deep Learning

Train a PointPillars network for object detection in point clouds.

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Automate Ground Truth Labeling For Vehicle Detection Using PointPillars

Automate vehicle detections in a point cloud using a pretrained PointPillars object detection network in the Lidar Labeler app. In this example, you use the AutomationAlgorithm interface to automate labeling in the Lidar Labeler app.

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Lane Detection in 3-D Lidar Point Cloud

Detect lanes in lidar point clouds. You can use the intensity values returned from lidar point clouds to detect ego vehicle lanes. You can further improve the lane detection by using a curve-fitting algorithm and tracking the curve parameters. Lidar lane detection enables you to build complex workflows like lane keep assist, lane departure warning, and adaptive cruise control for autonomous driving. A test vehicle collects the lidar data using a lidar sensor mounted on its rooftop.

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