【CVPR2018】Learning for Disparity Estimation through Feature Constancy

Different from existing methods that use multiple networks for different steps in stereo matching, we incorporate all step into a single network to enable end-to-end training. The proposed network consists of three parts: multi-scale shared feature extraction, initial disparity estimation and disparity refinement.

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