3D Reconstruction of Weak Texture Image Based on Tensor Voting and Dense Map Method

Authors

Mingwei Chen, 1. Kunming University of Science and Technology, Kunming 650500, China;;
Yang Yang, 2. Yunnan Air Traffic management Sub-bureau, Kunming 650200, China;

Abstract

Abstract: Image based 3D reconstruction method can't effectively detect and match feature points and can't robustly expand 3D point cloud in weak texture image region, due to the fact that weak texture has single color and local reflection phenomenon. It is possible to produce model hole and a large number of 3D noise points, and affect the accuracy and integrity of 3D reconstruction. Slack variables constraint was used to filter mismatch features optimizing the camera matrix, in the dense point cloud expansion stage by using tensor voting principle to filter noise 3d point which is inconsistent with three-dimensional point's normal around. Multi-scale discrete-continuous variational method was used to reconstruct 3D models. The experimental results show that the proposed method has better accuracy and integrity than the reconstruction methods of PMVS, MVE and MeshRecon.

Recommended Citation

Chen, Mingwei and Yang, Yang (2020) "3D Reconstruction of Weak Texture Image Based on Tensor Voting and Dense Map Method," Journal of System Simulation: Vol. 27: Iss. 10, Article 48.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss10/48

First Page

2553

Revised Date

2015-07-24

DOI Link

https://doi.org/

Last Page

2559

CLC

TP391.9

Recommended Citation

Chen Mingwei, Yang Yang. 3D Reconstruction of Weak Texture Image Based on Tensor Voting and Dense Map Method[J]. Journal of System Simulation, 2015, 27(10): 2553-2559.