  Despite recent advances, estimating optical flow remains a challenging
problem in the presence of illumination change, large occlusions or fast
movement. In this paper, we propose a novel optical flow estimation framework
which can provide accurate dense correspondence and occlusion localization
through a multi-scale generalized plane matching approach. In our method, we
regard the scene as a collection of planes at multiple scales, and for each
such plane, compensate motion in consensus to improve match quality. We
estimate the square patch plane distortion using a robust plane model detection
method and iteratively apply a plane matching scheme within a multi-scale
framework. During the flow estimation process, our enhanced plane matching
method also clearly localizes the occluded regions. In experiments on
MPI-Sintel datasets, our method robustly estimated optical flow from given
noisy correspondences, and also revealed the occluded regions accurately.
Compared to other state-of-the-art optical flow methods, our method shows
accurate occlusion localization, comparable optical flow quality, and better
thin object detection.
