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Robust Stereo Matching Using Probabilistic Laplacian Surface Propagation

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Computer Vision – ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9003))

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Abstract

This paper describes a probabilistic Laplacian surface propagation (PLSP) framework for a robust stereo matching under severe radiometric variations. We discover that a progressive scheme overcomes an inherent limitation for this task, while most conventional efforts have been focusing on designing a robust cost function. We propose the ground control surfaces (GCSs) designed as progressive unit, which alleviates the problems of conventional progressive methods and superpixel based methods, simultaneously. Moreover, we introduce a novel confidence measure for stereo pairs taken under radiometric variations based on the probability of correspondences. Specifically, the PLSP estimates the GCSs from initial sparse disparity maps using a weighted least-square. The GCSs are then propagated on a superpixel graph with a surface confidence weighting. Experimental results show that the PLSP outperforms state-of-the-art robust cost function based methods and other propagation methods for the stereo matching under radiometric variations.

Bumsub Ham—WILLOW project-team, Département d’Informatique de l’Ecole Normale Supérieure, ENS/Inria/CNRS UMR 8548.

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Notes

  1. 1.

    In order to evaluate the robustness of only surface propagation, the LSP only expands the propagation unit as a superpixel without the confidence weighting for GCSs.

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Acknowledgement

This research was supported by the MSIP(Ministry of Science, ICT and Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (NIPA-2014-H0301-14-1012) supervised by the NIPA(National IT Industry Promotion Agency).

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Authors and Affiliations

  1. Yonsei University, Seoul, Republic of Korea

    Seungryong Kim, Seungchul Ryu, Seon Joo Kim & Kwanghoon Sohn

  2. Inria, Paris, France

    Bumsub Ham

Authors
  1. Seungryong Kim
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  2. Bumsub Ham
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  3. Seungchul Ryu
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  4. Seon Joo Kim
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  5. Kwanghoon Sohn
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Corresponding author

Correspondence to Kwanghoon Sohn .

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Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Kim, S., Ham, B., Ryu, S., Kim, S.J., Sohn, K. (2015). Robust Stereo Matching Using Probabilistic Laplacian Surface Propagation. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision – ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9003. Springer, Cham. https://doi.org/10.1007/978-3-319-16865-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-16865-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16864-7

  • Online ISBN: 978-3-319-16865-4

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