结合超体素与颜色信息的区域生长点云分割方法
Region growing point cloud segmentation method combining supervoxel and color information
鲁斌 1王志远1
作者信息
- 1. 华北电力大学计算机系,河北保定 071000;复杂能源系统智能计算教育部工程研究中心计算机系,河北保定 071000
- 折叠
摘要
为解决传统区域生长点云分割算法存在的欠分割和过分割现象,提出一种结合超体素与颜色信息的区域生长点云分割方法.在分割过程中加入超体素过分割步骤,避免直接从点云中分割数据,有效消除噪声和异常值对分割的影响,利用一种几何和颜色信息的联合准则合并超体素并进行区域生长.与深度学习方法和其它3种传统分割算法相比,分割效率和精度都得到了较大提升,解决了欠分割和过分割的问题.
Abstract
To solve the problems of under segmentation and over segmentation in traditional region growing point cloud segmenta-tion algorithm,a point cloud segmentation algorithm combining supervoxel and color region growth was proposed.The super voxel over segmentation step was added in the segmentation process,which avoided the direct segmentation of data from the point cloud and decreased the effect of yawp and exception value on segmentation algorithm.A joint criterion of geometric and color information was used to merge the super voxels and grow the region.Compared with the depth learning method and the other three traditional segmentation algorithms,the segmentation efficiency and accuracy are greatly improved,and the problems of under segmentation and over segmentation are well solved.
关键词
超体素/法线信息/点云分割/区域生长/颜色信息/过分割/欠分割Key words
supervoxel/normal information/point cloud segmentation/region growing/color information/over segmentation/under segmentation引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据