Robust Monocular Epipolar Flow Estimation
We consider the problem of computing optical flow in monocular video taken from a moving vehicle. In this setting, the vast majority of image flow is due to the vehicle's ego-motion. We propose to take advantage of this fact and estimate flow along the epipolar lines of the egomotion. Towards this goal, we derive a slanted-plane MRF model which explicitly reasons about the ordering of planes and their physical validity at junctions. Furthermore, we present a bottom-up grouping algorithm which produces over-segmentations that respect flow boundaries. We demonstrate the effectiveness of our approach in the challenging KITTI flow benchmark [11] achieving half the error of the best competing general flow algorithm and one third of the error of the best epipolar flow algorithm.
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