  Optical flow estimation is one of the most studied problems in computer
vision, yet recent benchmark datasets continue to reveal problem areas of
today's approaches. Occlusions have remained one of the key challenges. In this
paper, we propose a symmetric optical flow method to address the well-known
chicken-and-egg relation between optical flow and occlusions. In contrast to
many state-of-the-art methods that consider occlusions as outliers, possibly
filtered out during post-processing, we highlight the importance of joint
occlusion reasoning in the optimization and show how to utilize occlusion as an
important cue for estimating optical flow. The key feature of our model is to
fully exploit the symmetry properties that characterize optical flow and
occlusions in the two consecutive images. Specifically through utilizing
forward-backward consistency and occlusion-disocclusion symmetry in the energy,
our model jointly estimates optical flow in both forward and backward
direction, as well as consistent occlusion maps in both views. We demonstrate
significant performance benefits on standard benchmarks, especially from the
occlusion-disocclusion symmetry. On the challenging KITTI dataset we report the
most accurate two-frame results to date.
