Surface Reconstruction of Point Cloud Based on Geometric Structure

Authors

Zhenfa Yang
Wan Gang
Xuefeng Cao
Li feng
Lixiang Xie

Abstract

Abstract: To solve the problems that the existing noise in dense point cloud generated from sequential images dense matching affects the accuracy, and geometric structure such as flat exists in urban scenes, a surface reconstruction method of dense point cloud based on geometric structure is proposed in this paper. RANSAC algorithm is applied to extract planar structure. The original points were structured into planar points, crease points of two planes intersecting, corner points of three or more planes intersecting and clutter points. Then the structured point cloud is divided into tetrahedron by 3D Delaunay, improving the penalty term of min-cut energy function by the triangular patches consisted by the four structured points to extract surface of the 3D Delaunay tetrahedron. The experiment results show that, compared with several classical methods and business software, the proposed method can reconstruct a real 3D scene and can recovery the scene geometry structure of plane.

Recommended Citation

Yang, Zhenfa; Gang, Wan; Cao, Xuefeng; feng, Li; and Xie, Lixiang (2020) "Surface Reconstruction of Point Cloud Based on Geometric Structure," Journal of System Simulation: Vol. 29: Iss. 11, Article 13.
DOI: 10.16182/j.issn1004731x.joss.201711013
Available at: https://dc-china-simulation.researchcommons.org/journal/vol29/iss11/13

First Page

2684

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201711013

Last Page

2692

CLC

TP274

Recommended Citation

Yang Zhenfa, Wan Gang, Cao Xuefeng, Li feng, Xie Lixiang. Surface Reconstruction of Point Cloud Based on Geometric Structure[J]. Journal of System Simulation, 2017, 29(11): 2684-2692.

DOI

10.16182/j.issn1004731x.joss.201711013