딥러닝을 사용한 라이다 처리

라이다 포인트 클라우드 처리를 위한 딥러닝 워크플로 확장

Deep Learning Toolbox™를 Lidar Toolbox™와 함께 사용하여 라이다 포인트 클라우드 데이터 처리에 딥러닝 알고리즘을 적용합니다.

앱

Label ground truth data in lidar point clouds

함수

객체 검출

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

분할

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

추천 예제

Lidar Object Detection Using Complex-YOLO v4 Network

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

라이브 스크립트 열기

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.

라이브 스크립트 열기

Lidar Point Cloud Semantic Segmentation Using PointSeg Deep Learning Network

Train a PointSeg semantic segmentation network on 3-D organized lidar point cloud data.

라이브 스크립트 열기

Lidar Point Cloud Semantic Segmentation Using SqueezeSegV2 Deep Learning Network

Train a SqueezeSegV2 semantic segmentation network on 3-D organized lidar point cloud data.

라이브 스크립트 열기

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 Semantic Segmentation Using SqueezeSegV2 Deep Learning Network (Lidar Toolbox). 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.

라이브 스크립트 열기

Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning

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

라이브 스크립트 열기

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++ (Lidar Toolbox).

라이브 스크립트 열기

Lidar 3-D Object Detection Using PointPillars Deep Learning

Train a PointPillars network for object detection in point clouds.

라이브 스크립트 열기

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 (Lidar Toolbox) 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 (Lidar Toolbox) in the Lidar Labeler (Lidar Toolbox). The example uses the AutomationAlgorithm interface in the Lidar Labeler app to automate labeling.