  Despite stereo matching accuracy has greatly improved by deep learning in the
last few years, recovering sharp boundaries and high-resolution outputs
efficiently remains challenging. In this paper, we propose Stereo Mixture
Density Networks (SMD-Nets), a simple yet effective learning framework
compatible with a wide class of 2D and 3D architectures which ameliorates both
issues. Specifically, we exploit bimodal mixture densities as output
representation and show that this allows for sharp and precise disparity
estimates near discontinuities while explicitly modeling the aleatoric
uncertainty inherent in the observations. Moreover, we formulate disparity
estimation as a continuous problem in the image domain, allowing our model to
query disparities at arbitrary spatial precision. We carry out comprehensive
experiments on a new high-resolution and highly realistic synthetic stereo
dataset, consisting of stereo pairs at 8Mpx resolution, as well as on
real-world stereo datasets. Our experiments demonstrate increased depth
accuracy near object boundaries and prediction of ultra high-resolution
disparity maps on standard GPUs. We demonstrate the flexibility of our
technique by improving the performance of a variety of stereo backbones.
