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.
|Title of host publication||Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers|
|Editors||Ian Reid, Ming-Hsuan Yang, Hideo Saito, Daniel Cremers|
|Number of pages||16|
|Publication status||Published - 2015|
|Event||12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore|
Duration: 2014 Nov 1 → 2014 Nov 5
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||12th Asian Conference on Computer Vision, ACCV 2014|
|Period||14/11/1 → 14/11/5|
Bibliographical notePublisher Copyright:
© Springer International Publishing Switzerland 2015.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)