  Our goal is to significantly speed up the runtime of current state-of-the-art
stereo algorithms to enable real-time inference. Towards this goal, we
developed a differentiable PatchMatch module that allows us to discard most
disparities without requiring full cost volume evaluation. We then exploit this
representation to learn which range to prune for each pixel. By progressively
reducing the search space and effectively propagating such information, we are
able to efficiently compute the cost volume for high likelihood hypotheses and
achieve savings in both memory and computation. Finally, an image guided
refinement module is exploited to further improve the performance. Since all
our components are differentiable, the full network can be trained end-to-end.
Our experiments show that our method achieves competitive results on KITTI and
SceneFlow datasets while running in real-time at 62ms.
