基于改进的局部表面凸性算法三维点云分割 -

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基于改进的局部表面凸性算法三维点云分割

doi:10.3788/CO.20171003.0348

王雅男^{1, 2,},
王挺峰¹,
田玉珍^1,,,
孙涛¹

1.
中国科学院长春光学精密机械与物理研究所与物质相互作用国家重点实验室, 吉林长春 130033
2.
中国科学院大学, 北京 100049

基金项目:

国家高技术研究发展计划（863计划）资助项目

详细信息

作者简介:
王雅男(1992-), 女, 山东德州人, 硕士研究生, 2014年于哈尔滨工业大学获得学士学位, 主要从事主动探测方面的研究。E-mail:arianwang@outlook.com
田玉珍(1985—)，男，内蒙古呼和浩特人，博士，助理研究员，主要从事主动照明成像、大气传输湍流效应等方面的研究。E-mail:tianyz@ciomp.ac.cn

通讯作者:
田玉珍, E-mail:tianyz@ciomp.ac.cn

中图分类号:TN958.98
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- 文章访问数:1477
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- 被引次数:0
出版历程
- 收稿日期:2017-01-23
- 修回日期:2017-03-30
- 刊出日期:2017-06-01

Improved local convexity algorithm of segmentation for 3D point cloud

1.
State Key Laboratory of Laser Interaction with Matter, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2.
University of Chinese Academy of Science, Beijing 100049, China

Funds:

Supported by National High-tech R & D Program of China

摘要

摘要:点云分割是点云分类、识别以及三维重建等处理的基础，分割结果对后续应用影响巨大。本文提出利用连通点集改进局部表面凸性算法中邻近点关系的方法，解决目前三维成像系统点云分割算法在处理复杂环境散乱点云时存在分割过度及分割不充分的问题，通过主顶点与周围点构成连通集，作为分割判断局部子点集，形成有效分割区域。该方法解决了常用点云分割方法无法对形状不规则物体进行有效分割的问题，提高了分割精度。算法实验结果表明，相比于最小切割算法和区域生长算法，基于连通点集的改进局部表面凸性算法对实际路面环境信息的分割效果更好，并能在一定程度上避免分割过度和分割不充分的情况，证明该方法适用于复杂环境散乱点云数据分割。
- 三维成像/
- 点云分割/
- 连通点集/
- 局部表面凸性
Abstract:Segmentation for point cloud is the basis of classification, recognition and reconstruction of point cloud datasets and the segmentation result plays an important role in following research. In this paper, we propose a method using connected point sets to analyze and improve the relationship between adjacent points in the local convexity segmentation, to solve problems of oversegmentation and undersegmentation when using the existing algorithms to segment scattered point cloud data in complex environment in 3D laser imaging system. By this method we use the main vertex and neighbors to constitute connected point sets which can be local point subsets of segmentation and form the effective segmented regions. The method solves the problem of the irregular object's segmentation, which can not be accomplished by common methods, and improves the accuracy of segmentation. Compared with the min-cut based segmentation and region growing segmentation, the improved local convexity segmentation of connected point sets is better for segmentation results of actual road information, and it can avoid oversegmentation and undersegmentation to some extent. It proved that this method is suitable for segmentation of scattered point cloud data in complex environment.
- three-dimensional laser imaging/
- segmentation for point cloud/
- connected point sets/
- local convexity

HTML全文

图 1局部表面凸性

Figure 1.Local surface convexity

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图 2两种连通点集

Figure 2.Two connected point sets

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图 33种算法对场景A进行分割的结果

Figure 3.Segmentation results of three algorithms for scene A

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图 43种算法对场景B进行分割的结果

Figure 4.Segmentation results of three algorithms for scene B

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图 5两种算法分割结果

Figure 5.Segmentation results of two algorithms

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表 1分割结果统计

Table 1.Statistics of segmentation result

	原始数据	区域生长算法	八连通局部表面凸性算法
数据点	123 398	102 809	110 766
行人	≥7	2	7
车辆	≥4	4	4
路灯	≥20	5	16

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