Fast Estimation of Large Displacement Optical Flow Using Dominant Motion Patterns & Sub-Volume PatchMatch Filtering

Abstract

This paper presents a new method for efficiently computing large-displacement optical flow. The method uses dominant motion patterns to identify a sparse set of sub-volumes within the cost volume and restricts subsequent Edge-Aware Filtering (EAF) to these sub-volumes. The method uses an extension of PatchMatch to filter these sub-volumes. The fact that our method only applies EAF to a small fraction of the entire cost volume boosts runtime performance. We also show that computational complexity is linear in the size of the images and does not depend upon the size of the label space. We evaluate the proposed technique on MPI Sintel, Middlebury and KITTI benchmarks and show that our method achieves accuracy comparable to those of several recent state-of-the-art methods, while posting significantly faster runtimes.