Feature-preserving simplification framework for 3D point cloud

被引：12

作者：

Xu, Xueli ^{[1
,2
,3
]}

Li, Kang ^{[1
,3
]}

Ma, Yifei ^{[1
,3
]}

Geng, Guohua ^{[1
,3
]}

Wang, Jingyu ^{[1
]}

Zhou, Mingquan ^{[1
,3
]}

Cao, Xin ^{[1
,3
]}

机构：

[1] Northwest Univ, Sch Informat Sci & Technol, Xian 710127, Shaanxi, Peoples R China

[2] Yanan Univ, Yanan 716000, Shaanxi, Peoples R China

[3] Natl & Local Joint Engn Res Ctr Cultural Heritage, Xian 710127, Shaanxi, Peoples R China

来源：

SCIENTIFIC REPORTS | 2022年 / 12卷 / 01期

关键词：

SURFACES;

D O I：

10.1038/s41598-022-13550-1

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

To obtain a higher simplification rate while retaining geometric features, a simplification framework for the point cloud is proposed. Firstly, multi-angle images of the original point cloud are obtained with a virtual camera. Then, feature lines of each image are extracted by deep neural network. Furthermore, according to the proposed mapping relationship between the acquired 2D feature lines and original point cloud, feature points of the point cloud are extracted automatically. Finally, the simplified point cloud is obtained by fusing feature points and simplified non-feature points. The proposed simplification method is applied to four data sets and compared with the other six algorithms. The experimental results demonstrate that our proposed simplification method has the superiority in terms of both retaining geometric features and high simplification rate.

引用

页数：15