segmentation -凯发k8网页登录

segment point cloud data using deep learning and geometric 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 cuboid, plane, and cylinder. lidar toolbox™ includes functions and workflows for geometric segmentation of point clouds. for more information, see the example.

lidar toolbox also supports semantic segmentation using deep learning. you can use the included pretrained pointseg, squeezesegv2, and pointnet convolutional neural networks (cnns) or develop custom segmentation models. for a segmentation workflow using a pointnet network, see .

semantic segmentation in lidar point clouds.

functions

geometrical

	segment ground from lidar data using a smrf algorithm
	segment organized 3-d range data into clusters
	segment ground points from organized lidar data
	segment curb points from point cloud
	segment point cloud into clusters based on euclidean distance

deep learning

load training data

	combine data from multiple datastores
	count occurrence of pixel or box labels
	ground truth label data
	datastore for image data
	datastore for pixel label data

augment and preprocess training data

	transform datastore
	sample 3-d bounding boxes and corresponding points from training data
	randomly augment point cloud data using objects

define layers

point cloud input layer

design networks

	point cloud input layer
	create squeezesegv2 segmentation network for organized lidar point cloud
	create pointnet segmentation network

segment point cloud

	point cloud semantic segmentation using deep learning
	semantic image segmentation using deep learning
	segment vegetation points from aerial lidar data
	segment building points from aerial lidar data

visualize results

	overlay label matrix regions on 2-d image
	plot 3-d point cloud

evaluate results

	evaluate semantic segmentation data set against ground truth
	confusion matrix of multi-class pixel-level image segmentation

topics

learn point cloud processing using deep learning.
assign class labels to each point inside a point cloud using deep learning.
define a pointnet network and use it to perform semantic segmentation.
(deep learning toolbox)
learn how to use datastores in deep learning applications.
(deep learning toolbox)
discover all the deep learning layers in matlab^®.