Segmentation

段使用深度学习和g点云数据eometric algorithms

语义分割将3-D点云中的每个点与类标签相关联，例如车,truck,地面, or植被。LIDAR Toolbox™提供深度学习算法，以对点云数据执行语义分割。使用PointSeg，Squeezesegv2和PointNet ++卷积神经网络（CNN）来开发语义分割模型。

You can segment ground in point cloud data using thesegmentGroundSMRFfunction. It is used in theTerrain Classification for Aerial Lidar Dataworkflow, which segments ground, vegetation and buildings in aerial point clouds.

Functions

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Geometrical

`segmentGroundSMRF`	Segment ground from lidar data using a Simple morphological filter (SMRF) algorithm
`segmentLidarData`	Segment organized 3-D range data into clusters
`segmentGroundFromLidarData`	Segment ground points from organized lidar data
`PCSEGDIST`	Segment point cloud into clusters based on Euclidean distance

深度学习

负载培训数据

`结合`	Combine data from multiple datastores
`CounteachLabel`	Count occurrence of pixel or box labels
`地面`	Ground truth label data
`成像`	Datastore for image data
`Pixellabeldatastore`	Datastore for pixel label data

Augment and Preprocess Training Data

`转换`	Transform datastore

设计网络

`Squeezesegv2layers`	Create SqueezeSegV2 segmentation network for organized lidar point cloud
`semanticseg`	使用深度学习的语义图像细分
`pointnetplusLayers`	Create PointNet++ segmentation network

Visualize Results

`labeloverlay`	Overlay label matrix regions on 2-D image
`PCSHOW`	图3-D点云

Evaluate Results

`evaluateSemanticSegmentation`	Evaluate semantic segmentation data set against ground truth
`segmentationConfusionMatrix`	多级像素级图像分割的混淆矩阵

Topics

使用深度学习开始开始点云

Understand how to use point clouds for deep learning.

Getting started with PointNet++

Define PointNet++ network and learn how to perform semantic segmentation using the same.

Datastores for Deep Learning(Deep Learning Toolbox)

了解如何在深度学习应用程序中使用数据存储。

List of Deep Learning Layers(Deep Learning Toolbox)

Discover all the deep learning layers in MATLAB^®。

Featured Examples

Lidar Point Cloud Semantic Segmentation Using PointSeg Deep Learning Network

在3D有组织的LiDar Point Cloud数据上训练Pointseg语义分割网络。

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Lidar Point Cloud Semantic Segmentation Using SqueezeSegV2 Deep Learning Network

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

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LIDAR点云分割网络的代码生成

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.

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使用PointNet ++深学习的空中激光雷达语义分割

训练PointNet ++深度学习网络，以对空中激光雷达数据进行语义分割。

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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++.

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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.

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Extract Forest Metrics and Individual Tree Attributes from Aerial Lidar Data

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

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