Results for CBMV
Submission data
Full name | CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation |
Description | We 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 title | CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation |
Publication authors | Konstantinos Batsos, Changjiang Cai, Philippos Mordohai |
Publication venue | Computer Vision and Pattern Recognition (CVPR) 2018 |
Publication URL | https://arxiv.org/abs/1804.01967 |
Programming language(s) | Python,C/C++, with CUDA |
Hardware | 6 cores @ 3.0 Ghz, Nvidia TitanX |
Website | https://github.com/kbatsos/CBMV |
Source code or download URL | https://github.com/kbatsos/CBMV |
Submission creation date | 13 Jan, 2018 |
Last edited | 10 Apr, 2018 |
High-res multi-view results
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Low-res many-view results
Info | all | low-res many-view | indoor | outdoor | lakeside | sand box | storage room | storage room 2 | tunnel |
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Low-res two-view results
SLAM results
all | boxes | boxes dark | buddha | cables 4 | cables 5 | desk 1 | desk 2 | desk changing 2 | desk dark 1 | desk dark 2 | desk global light changes | desk ir light | dino | drone | foreground occlusion | helmet | kidnap 2 | lamp | large loop 2 | large loop 3 | large non loop | motion 2 | motion 3 | motion 4 | planar 1 | reflective 2 | scale change | table 1 | table 2 | table 5 | table 6 | table global light changes | table local light changes | table scene | trashbin | |||
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