Apply deep learning algorithms to process Lidar point cloud data by using
Deep Learning Toolbox™ together with Lidar Toolbox™.
Train a PointSeg semantic segmentation network on 3-D organized lidar point cloud data.
Train a SqueezeSegV2 semantic segmentation network on 3-D organized lidar point cloud data.
Generate CUDA® MEX code for a deep learning network for lidar semantic segmentation. This example uses a pretrained SqueezeSegV2  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.
Train a PointNet++ deep learning network to perform semantic segmentation on aerial lidar data.
Generate CUDA® MEX code for a PointNet++  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++.
Train a PointPillars network for object detection in point clouds.
Generate CUDA® MEX for a PointPillars object detector with custom layers. For more information, see Lidar 3-D Object Detection Using PointPillars Deep Learning example from the Lidar Toolbox™.
Perform typical data augmentation techniques used in 3-D object detection workflows with lidar data.
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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