RecResNet: A Recurrent Residual CNN Architecture for Disparity Map Enhancement

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We present a neural network architecture applied to the problem of refining a dense disparity map generated by a stereo algorithm to which we have no access. Our approach is able to learn which disparity values should be modified and how, from a training set of images, estimated disparity maps and the corresponding ground truth. Its only input at test time is a disparity map and the reference image. Two design characteristics are critical for the success of our network: (i) it is formulated as a recurrent neural network, and (ii) it estimates the output refined disparity map as a combination of residuals computed at multiple scales, that is at different up-sampling and down-sampling rates. The first property allows the network, which we named RecResNet, to progressively improve the disparity map, while the second property allows the corrections to come from different scales of analysis, addressing different types of errors in the current disparity map. We present competitive quantitative and qualitative results on the KITTI 2012 and 2015 benchmarks that surpass the accuracy of previous disparity refinement methods.