  In this paper, we propose a unified method to jointly learn optical flow and
stereo matching. Our first intuition is stereo matching can be modeled as a
special case of optical flow, and we can leverage 3D geometry behind
stereoscopic videos to guide the learning of these two forms of
correspondences. We then enroll this knowledge into the state-of-the-art
self-supervised learning framework, and train one single network to estimate
both flow and stereo. Second, we unveil the bottlenecks in prior
self-supervised learning approaches, and propose to create a new set of
challenging proxy tasks to boost performance. These two insights yield a single
model that achieves the highest accuracy among all existing unsupervised flow
and stereo methods on KITTI 2012 and 2015 benchmarks. More remarkably, our
self-supervised method even outperforms several state-of-the-art fully
supervised methods, including PWC-Net and FlowNet2 on KITTI 2012.
