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We present an improved three-step pipeline for the stereo matching problemand introduce multiple novelties at each stage. We propose a new highwaynetwork architecture for computing the matching cost at each possibledisparity, based on multilevel weighted residual shortcuts, trained with ahybrid loss that supports multilevel comparison of image patches. A novelpost-processing step is then introduced, which employs a second deepconvolutional neural network for pooling global information from multipledisparities. This network outputs both the image disparity map, which replacesthe conventional "winner takes all" strategy, and a confidence in theprediction. The confidence score is achieved by training the network with a newtechnique that we call the reflective loss. Lastly, the learned confidence isemployed in order to better detect outliers in the refinement step. Theproposed pipeline achieves state of the art accuracy on the largest and mostcompetitive stereo benchmarks, and the learned confidence is shown tooutperform all existing alternatives.

Improved Stereo Matching with Constant Highway Networks and Reflective Confidence Learning