Scattered Point Cloud Simplification Algorithm Integrating k-means Clustering and Hausdorff Distance

被引：0
作者：
Li J. [1 ]
Cao Y. [2 ]
Wang Z. [2 ,3 ]
Wang G. [2 ]
机构：
[1] School of the Geo-Science &Technology, Zhengzhou University, Zhengzhou
[2] School of Water Conservancy and Environment, Zhengzhou University, Zhengzhou
[3] Zhongyuan University of Technology, Zhengzhou
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2020年 / 45卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Curved surface fitting; Hausdorff distance; K-means clustering; Point cloud simplification;
D O I：
10.13203/j.whugis20180204
中图分类号：
学科分类号：
摘要：
Aiming at the incomplete retention of features during the point cloud data procession by point cloud simplification algorithm, and data holes caused by small-curvature point cloud simplification algorithm, this paper proposes a new point cloud simplification algorithm integrated k-means clustering and Hausdorff distance. The topological adjacency is established in the new simplification algorithm based on the OcTree algorithm. Then the principal curvatures of all point cloud is calculated and the Hausdorff distance of the principal curvatures is calculated, and the Hausdorff distance threshold set by the requirements of the reduced target is used to extracted the point cloud feature. Finally, k-means clustering is performed on non-feature regions to extract feature points, and the extracted feature points are merged to obtain reduced results. Results show that the proposed algorithm can retain the feature information of the model more completely and avoid the void phenomena. © 2020, Research and Development Office of Wuhan University. All right reserved.
引用
收藏
页码：250 / 257
页数：7
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