Robust stereo matching using probabilistic laplacian surface propagation

Seungryong Kim, Bumsub Ham, Seungchul Ryu, Seon Joo Kim, Kwanghoon Sohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
EditorsIan Reid, Ming-Hsuan Yang, Hideo Saito, Daniel Cremers
PublisherSpringer Verlag
Pages368-383
Number of pages16
ISBN (Electronic)9783319168647
DOIs
Publication statusPublished - 2015 Jan 1
Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
Duration: 2014 Nov 12014 Nov 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9003
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th Asian Conference on Computer Vision, ACCV 2014
CountrySingapore
CitySingapore
Period14/11/114/11/5

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kim, S., Ham, B., Ryu, S., Kim, S. J., & Sohn, K. (2015). Robust stereo matching using probabilistic laplacian surface propagation. In I. Reid, M-H. Yang, H. Saito, & D. Cremers (Eds.), Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers (pp. 368-383). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9003). Springer Verlag. https://doi.org/10.1007/978-3-319-16865-4_24