Research Article
Global Depth Refinement Based on Patches
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@INPROCEEDINGS{10.1007/978-3-319-73564-1_42, author={Xu Huang and Yanfeng Zhang and Gang Zhou and Lu Liu and Gangshan Cai}, title={Global Depth Refinement Based on Patches}, proceedings={Machine Learning and Intelligent Communications. Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I}, proceedings_a={MLICOM}, year={2018}, month={2}, keywords={1D label 3D label Fronto-parallel bias Patch Global optimization}, doi={10.1007/978-3-319-73564-1_42} }- Xu Huang
Yanfeng Zhang
Gang Zhou
Lu Liu
Gangshan Cai
Year: 2018
Global Depth Refinement Based on Patches
MLICOM
Springer
DOI: 10.1007/978-3-319-73564-1_42
Abstract
Current stereo matching methods can be divided into 1D label algorithms and 3D label algorithms. 1D label algorithms are simple and fast, but they can’t aovid fronto-parallel bias. 3D label algorithms can solve fronto-parallel bias. However, they are very time-consuming. In order to avoid fronto-parallel bias efficiently, this paper introduces a new global depth refinement based on patches. The method transforms the depth optimization problem into a quadratic function computation, which has a low time complexity. Experiments on Motorcycle imagery and Wuhan university imagery verify the correctness and the effectiveness of the proposed method.
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