Point Cluster Analysis Using a 3D Voronoi Diagram with Applications in Point Cloud Segmentation

ISPRS International Journal of Geo-Information

Shen Ying³  Guang Xu¹  Chengpeng Li³  Zhengyuan Mao² 

[1] State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; E-Mail:;National Engineering Research Center of Geospatial Information Technology, Fuzhou University, Fuzhou 350002, China; E-Mail:;School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China; E-Mail:

关键词: 3D Voronoi diagram;    spatial cluster;    point cloud segmentation;   

DOI  :  10.3390/ijgi4031480

来源: mdpi

【 摘 要 】

Three-dimensional (3D) point analysis and visualization is one of the most effective methods of point cluster detection and segmentation in geospatial datasets. However, serious scattering and clotting characteristics interfere with the visual detection of 3D point clusters. To overcome this problem, this study proposes the use of 3D Voronoi diagrams to analyze and visualize 3D points instead of the original data item. The proposed algorithm computes the cluster of 3D points by applying a set of 3D Voronoi cells to describe and quantify 3D points. The decompositions of point cloud of 3D models are guided by the 3D Voronoi cell parameters. The parameter values are mapped from the Voronoi cells to 3D points to show the spatial pattern and relationships; thus, a 3D point cluster pattern can be highlighted and easily recognized. To capture different cluster patterns, continuous progressive clusters and segmentations are tested. The 3D spatial relationship is shown to facilitate cluster detection. Furthermore, the generated segmentations of real 3D data cases are exploited to demonstrate the feasibility of our approach in detecting different spatial clusters for continuous point cloud segmentation.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.