Lidar Processing

Extend deep learning workflows for Lidar point cloud processing

Apply deep learning algorithms to process Lidar point cloud data by using Deep Learning Toolbox™ together with Lidar Toolbox™.

Apps

Lidar Labeler

Label ground truth data in lidar point clouds

Functions

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Object Detection

`pointPillarsObjectDetector`	PointPillars object detector
`trainPointPillarsObjectDetector`	Train PointPillars object detector
`detect`	Detect objects using PointPillars object detector

Segmentation

`squeezesegv2Layers`	Create SqueezeSegV2 segmentation network for organized lidar point cloud
`pointnetplusLayers`	Create PointNet++ segmentation network

Featured Examples

Lidar Object Detection Using Complex-YOLO v4 Network

Train a Complex-YOLO v4 network to perform object detection on point clouds.

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Code Generation for Lidar Object Detection Using SqueezeSegV2 Network

Generate CUDA® MEX code for a lidar object detection network. In the example, you first segment the point cloud with a pretrained network, then cluster the points and fit 3-D bounding boxes to each cluster. Finally, you generate MEX code for the network.

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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

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

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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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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Code Generation For Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning

Generate CUDA® MEX code for a PointNet++ [1] network for lidar semantic segmentation. This example uses a pretrained PointNet++ network that can segment unorganized lidar point clouds belonging to eight classes (buildings, cars, trucks, poles, power lines, fences, ground, and vegetation). For more information on PointNet++ network, see Getting Started with PointNet++.

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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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Code Generation For Lidar Object Detection Using PointPillars Deep Learning

Generate CUDA® MEX for a PointPillars object detector. For more information, see Lidar 3-D Object Detection Using PointPillars Deep Learning example from the Lidar Toolbox™.

Data Augmentations for Lidar Object Detection Using Deep Learning

Perform typical data augmentation techniques for 3-D object detection workflows with lidar data.

Automate Ground Truth Labeling For Vehicle Detection Using PointPillars

Automate vehicle detections in a point cloud using a pretrained pointPillarsObjectDetector in the Lidar Labeler app. The example uses the AutomationAlgorithm interface in the Lidar Labeler app to automate labeling.