Algorithm for 3D Point Cloud Denoising

被引：10
|
作者：
Huang Wenming [1 ]
Li Yuanwang [1 ]
Wen Peizhi [1 ]
Wu Xiaojun [2 ]
机构：
[1] Guilin Univ Elect Technol, Sch Comp & Control, Guilin, Peoples R China
[2] Shenzhen Grad Sch, Harbin Inst Technol, Shenzhen, Peoples R China
来源：
THIRD INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING | 2009年
基金：
中国国家自然科学基金;
关键词：
point cloud data; data preprocessing; domain-denoising;
D O I：
10.1109/WGEC.2009.139
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The raw data of point cloud produced by 3D scanning tools contains additive noise from various sources. This paper proposes a method for 3D unorganized point cloud denoising by making full use of the depth information of unorganized points and space analytic geometry theory, applying over-domain average method for 2D image of image denoising theory to 3D point data. The point cloud noises are filtered by using irregular polyhedron based on the limited local neighborhoods. The experiment shows that the proposed method successfully removes noise from point cloud with the features of the scattered point model reserved. Furthermore, the presented algorithm excels in its simplicity both in implementation and operation.
引用
收藏
页码：574 / +
页数：2
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