Journal
IEEE ACCESS
Volume 6, Issue -, Pages 27971-27983Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2838442
Keywords
Stereo matching; matching cost computation; multiscale feature extraction; convolutional neural networks
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Funding
- Cross-Ministry Giga KOREA Project - Korean Government (MSIT) (development of 4D reconstruction and dynamic deformable action model based hyper-realistic service technology) [GK18P0200]
- National Research Foundation of Korea - Korean Government (MSIP) [NRF-2015R1A2A1A10055037, NRF-2018R1A2B3003896]
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We propose four efficient feature extractors based on convolutional neural networks for stereo matching cost computation. Two of them generate multiscale features with diverse receptivefield sizes. These multiscale features are used to compute the corresponding multiscale matching costs. We then determine an optimal cost by combining the multiscale costs using edge information. On the other hand, the other two feature extractors produce uni-scale features by combining multiscale features directly through fully connected layers. Finally, after obtaining matching costs using one of the four extractors, we determine optimal disparities based on the cross-based cost aggregation and the semiglobal matching. Extensive experiments on the Middlebury stereo data sets demonstrate the effectiveness and efficiency of the proposed algorithm. Specifically, the proposed algorithm provides competitive matching performance with the state of the arts, while demanding lower computational complexity.
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