PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

被引:58
作者
Lu, Xiaohu [1 ]
Yao, Jian [1 ]
Tu, Jinge [1 ]
Li, Kai [1 ]
Li, Li [1 ]
Liu, Yahui [1 ]
机构
[1] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan, Hubei, Peoples R China
来源
XXIII ISPRS CONGRESS, COMMISSION III | 2016年 / 3卷 / 03期
关键词
Pairwise Linkage; Clustering; Point Cloud Segmentation; Slice Merging; SURFACE MESH SEGMENTATION; ALGORITHM;
D O I
10.5194/isprsannals-III-3-201-2016
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slice merging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.
引用
收藏
页码:201 / 208
页数:8
相关论文
共 29 条
[1]  
[Anonymous], 2010, ANN: a library for approximate nearest neighbor searching
[2]   An optimal algorithm for approximate nearest neighbor searching in fixed dimensions [J].
Arya, S ;
Mount, DM ;
Netanyahu, NS ;
Silverman, R ;
Wu, AY .
JOURNAL OF THE ACM, 1998, 45 (06) :891-923
[3]  
Belton D., 2006, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, V36, P44
[4]   Segmentation methods for smooth point regions of conventional engineering objects [J].
Benko, P ;
Várady, T .
COMPUTER-AIDED DESIGN, 2004, 36 (06) :511-523
[6]  
CASTILLO E, 2013, Innovations for shape analysis. Mathematics and visualization, DOI 10.1007/978-3-642-34141-0_13
[7]   Mean shift: A robust approach toward feature space analysis [J].
Comaniciu, D ;
Meer, P .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2002, 24 (05) :603-619
[8]  
Ester M, 1996, P 2 INT C KNOWLEDGE, DOI DOI 10.5555/3001460.3001507
[9]  
Harati A., 2007, IFAC S ONINTELLIGENT, V40, P475
[10]   Three-dimensional surface mesh segmentation using curvedness-based region growing approach [J].
Jagannathan, Anupama ;
Miller, Eric L. .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2007, 29 (12) :2195-2204