Lidar Processing Using Deep Learning

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

squeezesegv2Layers Create SqueezeSegV2 segmentation network for organized lidar point cloud

Featured Examples

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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