Segmentation and Detection Using Deep Learning

Semantic segmentation and object detection in point cloud data using deep learning algorithms

Semantic segmentation associates each point in a 3-D point cloud with a class label, such as car, truck, ground, or vegetation. Lidar Toolbox™ provides deep learning algorithms to perform semantic segmentation on point cloud data. Use PointSeg and SqueezeSegV2 convolutional neural networks (CNN) to develop semantic segmentation models.

Lidar Toolbox also provides the object detection CNN PointPillars for developing custom object detection models.

Functions

expand all

combineCombine data from multiple datastores
countEachLabelCount occurrence of pixel or box labels
imageDatastoreDatastore for image data
pixelLabelDatastoreDatastore for pixel label data
boxLabelDatastoreDatastore for bounding box label data
fileDatastoreDatastore with custom file reader
transformTransform datastore
randomAffine3dCreate randomized 3-D affine transformation
bboxwarpApply geometric transformation to bounding boxes
squeezesegv2LayersCreate SqueezeSegV2 segmentation network for organized lidar point cloud
semanticsegSemantic image segmentation using deep learning
labeloverlayOverlay label matrix regions on 2-D image
showShapeDisplay shapes on image, video, or point cloud
pcshowPlot 3-D point cloud
evaluateSemanticSegmentationEvaluate semantic segmentation data set against ground truth
evaluateDetectionAOSEvaluate average orientation similarity metric for object detection
bboxOverlapRatioCompute bounding box overlap ratio

Topics

Getting Started with Point Clouds Using Deep Learning

Understand how to use point clouds for deep learning.

Featured Examples

Lidar Point Cloud Semantic Segmentation Using PointSeg Deep Learning Network

Lidar Point Cloud Semantic Segmentation Using PointSeg Deep Learning Network

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

Open Live Script
Lidar Point Cloud Semantic Segmentation Using SqueezeSegV2 Deep Learning Network

Lidar Point Cloud Semantic Segmentation Using SqueezeSegV2 Deep Learning Network

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

Open Live Script
Code Generation for Lidar Point Cloud Segmentation Network

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.

Open Live Script
Lidar 3-D Object Detection Using PointPillars Deep Learning

Lidar 3-D Object Detection Using PointPillars Deep Learning

Train a PointPillars network for object detection in point clouds.

Open Live Script

Lidar Toolbox Documentation

Support