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Optical flow estimation is one of the most studied problems in computervision, yet recent benchmark datasets continue to reveal problem areas oftoday's approaches. Occlusions have remained one of the key challenges. In thispaper, we propose a symmetric optical flow method to address the well-knownchicken-and-egg relation between optical flow and occlusions. In contrast tomany state-of-the-art methods that consider occlusions as outliers, possiblyfiltered out during post-processing, we highlight the importance of jointocclusion reasoning in the optimization and show how to utilize occlusion as animportant cue for estimating optical flow. The key feature of our model is tofully exploit the symmetry properties that characterize optical flow andocclusions in the two consecutive images. Specifically through utilizingforward-backward consistency and occlusion-disocclusion symmetry in the energy,our model jointly estimates optical flow in both forward and backwarddirection, as well as consistent occlusion maps in both views. We demonstratesignificant performance benefits on standard benchmarks, especially from theocclusion-disocclusion symmetry. On the challenging KITTI dataset we report themost accurate two-frame results to date.

MirrorFlow: Exploiting Symmetries in Joint Optical Flow and Occlusion Estimation