Segmentation

Segment point cloud data into clusters using deep learning algorithms

Semantic segmentation clusters the points of a 3-D point cloud by using their similar characteristics, and associates each point with a class label such as car, building, ground, or vegetation.

You can segment a point cloud based on edges, neighboring point properties, and geometric shapes such as a cuboid, plane, or cylinder. For more information on the segmentation process, see the Terrain Classification for Aerial Lidar Data example.

Lidar Toolbox™ functions also support semantic segmentation using deep learning. You can use the included pretrained RandLA-Net, Segment Anything Model (SAM), PointSeg, SqueezeSegV2, and PointNet++ convolutional neural networks (CNNs) or develop custom segmentation models. For an example of the segmentation process using a RandLA-Net network, see Aerial Lidar Semantic Segmentation Using RandLANet Deep Learning.

Semantic segmentation in lidar point clouds.

Functions

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pcsegdistSegment point cloud into clusters based on Euclidean distance
pcsegplanesSegment point cloud into planar surfaces (Since R2024b)
segmentLidarDataSegment organized 3-D range data into clusters
segmentGroundSMRFSegment ground from lidar data using a SMRF algorithm (Since R2021a)
segmentGroundFromLidarDataSegment ground points from organized lidar data
segmentCurbPointsSegment curb points from point cloud (Since R2022b)

Prepare Training Data

groundTruthLidarObject for storing labeled lidar data (Since R2020b)
fileDatastoreDatastore with custom file reader
pixelLabelDatastoreDatastore for pixel label data
combineCombine data from multiple datastores
countEachLabelCount occurrence of pixel or box labels

Augment and Preprocess Training Data

transformTransform datastore
pctransformTransform 3-D point cloud
bboxwarpApply geometric transformation to bounding boxes

Create Deep Learning Networks

pointCloudInputLayerPoint cloud input layer (Since R2022b)
squeezesegv2NetworkCreate SqueezeSegV2 segmentation network for organized lidar point cloud (Since R2024a)
pointnetplusNetworkCreate PointNet++ segmentation network (Since R2024a)

Segment Point Cloud

randlanet Segment point clouds using RandLA-Net semantic segmentation network (Since R2024a)
trainRandlanetTrain RandLA-Net network to perform semantic segmentation (Since R2024a)
segmentAnythingAerialLidarPerform semantic segmentation of aerial lidar data using pretrained Segment Anything Model (SAM) (Since R2024b)
pcsegsamSegment all objects automatically in lidar point cloud using Segment Anything Model (SAM) (Since R2024b)
pcsemanticsegPoint cloud semantic segmentation using deep learning (Since R2022b)
segmentAerialLidarVegetationSegment vegetation points from aerial lidar data (Since R2022b)
segmentAerialLidarBuildingsSegment building points from aerial lidar data (Since R2022b)
segmentAerialLidarPowerlineSegment powerline points from aerial lidar data (Since R2023b)

Evaluate Results

evaluateSemanticSegmentationEvaluate semantic segmentation data set against ground truth
segmentationConfusionMatrixConfusion matrix of multi-class pixel-level image segmentation (Since R2020b)
labeloverlayOverlay label matrix regions on 2-D image

Topics

Featured Examples

Aerial Lidar Semantic Segmentation Using RandLANet Deep Learning

Aerial Lidar Semantic Segmentation Using RandLANet Deep Learning

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

Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning

Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning

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

Code Generation for Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning

Code Generation for Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning

Generate CUDA® MEX code for a PointNet++ network for lidar semantic segmentation.

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.

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.

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.

Data Augmentations for Lidar Object Detection Using Deep Learning

Data Augmentations for Lidar Object Detection Using Deep Learning

Perform typical data augmentation techniques for 3-D object detection workflows with lidar data.

Terrain Classification for Aerial Lidar Data

Terrain Classification for Aerial Lidar Data

Segment and classify terrain in aerial lidar data as ground, building, and vegetation. The example uses a LAZ file captured by an airborne lidar system as input. First, classify the point cloud data in the LAZ file into ground and non-ground points. Then, further classify non-ground points into building and vegetation points based on normals and curvature features. This figure provides an overview of the process.

Classify Land Cover Using Hyperspectral and Lidar Data

Classify Land Cover Using Hyperspectral and Lidar Data

Classify land cover using a fusion of hyperspectral and lidar data.

Extract Individual Tree Attributes and Forest Metrics from Aerial Lidar Data

Extract Individual Tree Attributes and Forest Metrics from Aerial Lidar Data

Extract individual tree attributes and forest metrics from aerial lidar data.