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Segment point cloud data using deep learning and geometric 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, SqueezeSegV2, and PointNet++ convolutional neural networks (CNN) to develop semantic segmentation models.

You can segment ground in point cloud data using the segmentGroundSMRF function. It is used in the Terrain Classification for Aerial Lidar Data workflow, which segments ground, vegetation and buildings in aerial point clouds.


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segmentGroundSMRFSegment ground from lidar data using SMRF algorithm
segmentLidarDataSegment organized 3-D range data into clusters
segmentGroundFromLidarDataSegment ground points from organized lidar data
pcsegdistSegment point cloud into clusters based on Euclidean distance

Load Training Data

combineCombine data from multiple datastores
countEachLabelCount occurrence of pixel or box labels
groundTruthGround truth label data
imageDatastoreDatastore for image data
pixelLabelDatastoreDatastore for pixel label data

Augment and Preprocess Training Data

transformTransform datastore

Design Deep Learning Networks

squeezesegv2LayersCreate SqueezeSegV2 segmentation network for organized lidar point cloud
semanticsegSemantic image segmentation using deep learning

Visualize Results

labeloverlayOverlay label matrix regions on 2-D image
pcshowPlot 3-D point cloud

Evaluate Results

evaluateSemanticSegmentationEvaluate semantic segmentation data set against ground truth
segmentationConfusionMatrixConfusion matrix of multi-class pixel-level image segmentation


Getting Started with Point Clouds Using Deep Learning

Understand how to use point clouds for deep learning.

Datastores for Deep Learning (Deep Learning Toolbox)

Learn how to use datastores in deep learning applications.

List of Deep Learning Layers (Deep Learning Toolbox)

Discover all the deep learning layers in MATLAB®.

Featured Examples