An Investigation of the High Efficiency Estimation Approach of the Large-Scale Scattered Point Cloud Normal Vector

被引：4

作者：

Meng, Xianglin ^{[1
]}

He, Wantao ^{[2
]}

Liu, Junyan ^{[3
]}

机构：

[1] Heilongjiang Univ Sci & Technol, Sch Mech Engn, Harbin 150027, Heilongjiang, Peoples R China

[2] Huazhong Univ Sci & Technol, State Key Lab Mat Proc & Die & Mould Technol, Wuhan 430074, Hubei, Peoples R China

[3] Harbin Inst Technol, Sch Mechatron Engn, Harbin 150001, Heilongjiang, Peoples R China

来源：

APPLIED SCIENCES-BASEL | 2018年 / 8卷 / 03期

基金：

黑龙江省自然科学基金; 中国国家自然科学基金;

关键词：

normal vector; large-scale; scattered point cloud; hash table; interpolation; ELLIPTIC GABRIEL GRAPH; NEAREST NEIGHBORS; ALGORITHM;

D O I：

10.3390/app8030454

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

The normal vector estimation of the large-scale scattered point cloud (LSSPC) plays an important role in point-based shape editing. However, the normal vector estimation for LSSPC cannot meet the great challenge of the sharp increase of the point cloud that is mainly attributed to its low computational efficiency. In this paper, a novel, fast method-based on bi-linear interpolation is reported on the normal vector estimation for LSSPC. We divide the point sets into many small cubes to speed up the local point search and construct interpolation nodes on the isosurface expressed by the point cloud. On the premise of calculating the normal vectors of these interpolated nodes, a normal vector bi-linear interpolation of the points in the cube is realized. The proposed approach has the merits of accurate, simple, and high efficiency, because the algorithm only needs to search neighbor and calculates normal vectors for interpolation nodes that are usually far less than the point cloud. The experimental results of several real and simulated point sets show that our method is over three times faster than the Elliptic Gabriel Graph-based method, and the average deviation is less than 0.01 mm.

引用

页数：11