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Multiple candidates and multiple constraints based accurate depth estimation for multi-view stereo
Author(s): Chao Zhang; Fugen Zhou; Bindang Xue
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Paper Abstract

In this paper, we propose a depth estimation method for multi-view image sequence. To enhance the accuracy of dense matching and reduce the inaccurate matching which is produced by inaccurate feature description, we select multiple matching points to build candidate matching sets. Then we compute an optimal depth from a candidate matching set which satisfies multiple constraints (epipolar constraint, similarity constraint and depth consistency constraint). To further increase the accuracy of depth estimation, depth consistency constraint of neighbor pixels is used to filter the inaccurate matching. On this basis, in order to get more complete depth map, depth diffusion is performed by neighbor pixels’ depth consistency constraint. Through experiments on the benchmark datasets for multiple view stereo, we demonstrate the superiority of proposed method over the state-of-the-art method in terms of accuracy.

Paper Details

Date Published: 8 February 2017
PDF: 5 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102251O (8 February 2017); doi: 10.1117/12.2267110
Show Author Affiliations
Chao Zhang, BeiHang Univ. (China)
Fugen Zhou, BeiHang Univ. (China)
Bindang Xue, BeiHang Univ. (China)


Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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