Submitted by Changjiang Cai.

Submission data

Full nameCBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
DescriptionWe generate a matching cost volume leveraging both data with ground truth and conventional wisdom. We accomplish this by coalescing diverse evidence from a bidirectional matching process via random forest classifiers. We show that the resulting matching volume estimation method achieves similar accuracy to purely data-driven alternatives on benchmarks and that it generalizes to unseen data much better. For more details, please check our paper.
Parameters* pi1 = 2.4
* pi2 = 24.25
* sgm_q1=3.3
* sgm_q2=1.4
* alpha=2.9
* tau_so=0.16 (There is a detailed setup about the parameters in our paper)
Publication titleCBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
Publication authorsKonstantinos Batsos, Changjiang Cai, Philippos Mordohai
Publication venueComputer Vision and Pattern Recognition (CVPR) 2018
Publication URLhttps://arxiv.org/abs/1804.01967
Programming language(s)Python,C/C++, with CUDA
Hardware6 cores @ 3.0 Ghz, Nvidia TitanX
Websitehttps://github.com/kbatsos/CBMV
Source code or download URLhttps://github.com/kbatsos/CBMV
Submission creation date13 Jan, 2018
Last edited10 Apr, 2018

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