Point cloud data set construction method and device based on statistics and concavity and convexity

Abstract

The invention relates to the technical field of computer vision and point cloud segmentation, and provides a point cloud data set construction method and device based on statistics and concavity and convexity. A traditional point cloud segmentation method is applied to construction of a deep learning data set, and the problem that the deep learning point cloud data set is deficient is solved. According to the main scheme, the method comprises the following steps: step 1, obtaining target original point cloud data, and carrying out feature-based filtering and denoising; step 2, performing first clustering, and performing super-body clustering over-segmentation on the point cloud to obtain a voxel block set; step 3, clustering for the second time: performing LCCP clustering on each voxel block obtained in the step 2 to obtain an LCCP clustering set; step 4, third clustering: performing conditional Euclidean clustering on the LCCP clustering set based on the point feature histogram to obtain a final clustering set; and 5, marking the point cloud according to the result of the final clustering set, and organizing the file to obtain a point cloud data set.

Description

technical field [0001] The invention relates to the technical fields of computer vision and point cloud segmentation, and provides a method and device for constructing a point cloud data set based on statistics and concave-convexity. Background technique [0002] In recent years, the popularity of depth sensors and 3D laser scanners has promoted the rapid development of 3D point cloud processing methods. As the basic technology of point cloud data processing and analysis, point cloud segmentation has become a research hotspot in the fields of automatic driving, navigation and positioning, smart city, and medical image segmentation. [0003] Point cloud segmentation is a technology that divides the point cloud into several specific regions with unique properties and identifies the content of the point cloud. Most of the traditional point cloud segmentation methods manually extract features by extracting the spatial distribution of 3D shape geometric attributes, and construct...

AI Technical Summary

Problems solved by technology

[0018] The purpose of the present invention is to apply the traditional point cloud segmentation method to the construction of deep learning data sets to solve the problem of scarcity of deep learning point cloud data sets

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