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We leverage knowledge of the problem's\ngeometry to form a cost volume using deep feature representations. We learn to\nincorporate contextual information using 3-D convolutions over this volume.\nDisparity values are regressed from the cost volume using a proposed\ndifferentiable soft argmin operation, which allows us to train our method\nend-to-end to sub-pixel accuracy without any additional post-processing or\nregularization. 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