  We present a new loss function for joint disparity and uncertainty estimation
in deep stereo matching. Our work is motivated by the need for precise
uncertainty estimates and the observation that multi-task learning often leads
to improved performance in all tasks. We show that this can be achieved by
requiring the distribution of uncertainty to match the distribution of
disparity errors via a KL divergence term in the network's loss function. A
differentiable soft-histogramming technique is used to approximate the
distributions so that they can be used in the loss. We experimentally assess
the effectiveness of our approach and observe significant improvements in both
disparity and uncertainty prediction on large datasets.
