Point cloud simplification based on the information entropy of normal vector angle

被引：1
|
作者：
Chen, Xijiang [1 ]
Zhang, Guang [1 ]
Hua, Xianghong [2 ,3 ]
机构：
[1] School of Resource & Environment Engineering, Wuhan University of Technology, Wuhan, 430079, Hubei
[2] School of Geodesy & Geomatics, Wuhan University, Wuhan, 430079, Hubei,
[3] 3Hazard Monitoring & Prevention Research Center, Wuhan University, Wuhan, 430079, Hubei
来源：
Zhongguo Jiguang/Chinese Journal of Lasers | 2015年 / 42卷 / 08期
关键词：
Error entropy; Normal vector; Point cloud simplification; Remote sensing;
D O I：
10.3788/CJL201542.0814003
中图分类号：
学科分类号：
摘要：
A point cloud simplification based on the information entropy of normal vector angle is proposed, in view of the difficulty to ensure the optimal of precision and speed of simplification. The principal component analysis is used to estimate the normal of each point and the angle between normal vector and reference plane is computed. The K-nearest neighbor search algorithm is used to determine K-nearest neighbor points, and the local entropy of normal vector angle is proposed according to information entropy. The local entropy represents the features of surface. The point cloud is gradually simplified according to the different local entropy, the more points of convex region are retained and more points of plane are simplified, the non-uniform simplification is realized. The experimental results show that the proposed method can achieve a balance of precision and speed of simplification. ©, 2015, Science Press. All right reserved.
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页数：9
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