Performance evaluation of narrow band methods for variational stereo reconstruction
Stangl F, Souiai M, Cremers D (2013)
Publication Type: Conference contribution
Publication year: 2013
Journal
Book Volume: 8142 LNCS
Pages Range: 194-204
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: DEU
DOI: 10.1007/978-3-642-40602-7_20
Abstract
Convex relaxation techniques allow computing optimal or near-optimal solutions for a variety of multilabel problems in computer vision. Unfortunately, they are quite demanding in terms of memory and computation time making them unpractical for large-scale problems. In this paper, we systematically evaluate to what extent narrow band methods can be employed in order to improve the performance of variational multilabel optimization methods. We review variational methods, we present a narrow band formulation and demonstrate with a number of quantitative experiments that the narrow band formulation leads to a reduction in memory and computation time by orders of magnitude while preserving almost the same quality of results. In particular, we show that this formulation allows computing stereo depth maps for 6 Mpixels aerial image pairs on a single GPU in around one minute. © 2013 Springer-Verlag.
Involved external institutions
Germany (DE)How to cite
APA:
Stangl, F., Souiai, M., & Cremers, D. (2013). Performance evaluation of narrow band methods for variational stereo reconstruction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 194-204). DEU.
MLA:
Stangl, Franz, Mohamed Souiai, and Daniel Cremers. "Performance evaluation of narrow band methods for variational stereo reconstruction." Proceedings of the 35th German Conference on Pattern Recognition, GCPR 2013, DEU 2013. 194-204.
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