CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Detail preserved surface reconstruction from point cloud
Zhou Y(周洋)1,2; Shen SH(申抒含)1,2; Hu ZY(胡占义)1,2
Source PublicationSensors
2019
Volume19Issue:6Pages:1278
Abstract

In this paper, we put forward a new method for surface reconstruction from image-based point clouds. In particular, we introduce a new visibility model for each line of sight to preserve scene details without decreasing the noise filtering ability. To make the proposed method suitable for point clouds with heavy noise, we introduce a new likelihood energy term to the total energy of the binary labeling problem of Delaunay tetrahedra, and we give its s-t graph implementation. Besides, we further improve the performance of the proposed method with the dense visibility technique, which helps to keep the object edge sharp. The experimental result shows that the proposed method rivalled the state-of-the-art methods in terms of accuracy and completeness, and performed better with reference to detail preservation.

KeywordComputer Vision 3d Reconstruction Point Cloud
Indexed BySCI
Language英语
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23567
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorShen SH(申抒含)
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhou Y,Shen SH,Hu ZY. Detail preserved surface reconstruction from point cloud[J]. Sensors,2019,19(6):1278.
APA Zhou Y,Shen SH,&Hu ZY.(2019).Detail preserved surface reconstruction from point cloud.Sensors,19(6),1278.
MLA Zhou Y,et al."Detail preserved surface reconstruction from point cloud".Sensors 19.6(2019):1278.
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