Robust Stereo Matching Using Probabilistic Laplacian Surface Propagation

  • Seungryong Kim
  • Bumsub Ham
  • Seungchul Ryu
  • Seon Joo Kim
  • Kwanghoon SohnEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9003)


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.


Stereo Image Stereo Match Stereo Pair Sift Feature Confidence Weighting 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Seungryong Kim
    • 1
  • Bumsub Ham
    • 2
  • Seungchul Ryu
    • 1
  • Seon Joo Kim
    • 1
  • Kwanghoon Sohn
    • 1
    Email author
  1. 1.Yonsei UniversitySeoulRepublic of Korea
  2. 2.InriaParisFrance

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