Train, test, and deploy deep learning networks on lidar point clouds for object detection and semantic segmentation.
With MATLAB and Simulink, you can:
- Preprocess lidar point clouds for applying deep learning algorithms
- Use the Lidar Labeler app to label lidar point clouds for object detection
- Handle large amounts of data for training, testing, and validation with datastores
- Generate C/C++ and CUDA codes for deep learning workflows for semantic segmentation and object detection on point cloud data
Why Use Deep Learning for Lidar?
Lidar Semantic Segmentation
Apply deep learning algorithms to segment lidar point clouds. Train, test, and evaluate semantic segmentation networks, including PointNet++, PointSeg, and SqueezeSegV2, on lidar data.
Object Detection on Lidar Point Clouds
Detect and fit oriented bounding boxes around objects in lidar point clouds and use them for object tracking or lidar labeling workflows. Design, train, and evaluate robust detectors such as PointPillars networks.
Label lidar point clouds for training deep learning models. Apply built-in or custom algorithms to automate lidar point cloud labeling with the Lidar Labeler app and evaluate automation algorithm performance.
Generate CUDA® MEX code for networks like PointPillars, SqueezeSegV2, and PointNet++ to deploy point cloud segmentation or object detection algorithms on GPUs.