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Global Depth Refinement Based on Patches

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Book cover Machine Learning and Intelligent Communications (MLICOM 2017)

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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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Acknowledgements

This work was supported by the Chinese Parasol Entrepreneurial Partner Project of Wuhan Engineering Science & Technology Institute (Grant No. gkwt006).

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Authors and Affiliations

  1. Wuhan Engineering Science and Technology Institute, Jiangda Road 30, Wuhan, China

    Xu Huang, Gang Zhou, Lu Liu & Gangshan Cai

  2. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China

    Yanfeng Zhang

Authors
  1. Xu Huang
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  2. Yanfeng Zhang
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  3. Gang Zhou
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  4. Lu Liu
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  5. Gangshan Cai
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Corresponding author

Correspondence to Xu Huang .

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Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Huang, X., Zhang, Y., Zhou, G., Liu, L., Cai, G. (2018). Global Depth Refinement Based on Patches. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_42

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  • DOI: https://doi.org/10.1007/978-3-319-73564-1_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73563-4

  • Online ISBN: 978-3-319-73564-1

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