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Optical flow estimation remains challenging due to untextured areas, motionboundaries, occlusions, and more. Thus, the estimated flow is not equallyreliable across the image. To that end, post-hoc confidence measures have beenintroduced to assess the per-pixel reliability of the flow. We overcome theartificial separation of optical flow and confidence estimation by introducinga method that jointly predicts optical flow and its underlying uncertainty.Starting from common energy-based formulations, we rely on the correspondingposterior distribution of the flow given the images. We derive a variationalinference scheme based on mean field, which incorporates best practices fromenergy minimization. An uncertainty measure is obtained along the flow at everypixel as the (marginal) entropy of the variational distribution. We demonstratethe flexibility of our probabilistic approach by applying it to two differentenergies and on two benchmarks. We not only obtain flow results that arecompetitive with the underlying energy minimization approach, but also areliable uncertainty measure that significantly outperforms existing post-hocapproaches.

ProbFlow: Joint Optical Flow and Uncertainty Estimation