Multi-Scale Generalized Plane Match for Optical Flow
This work addresses optical flow estimation for computer vision applications, offering incremental improvements in occlusion handling and thin object detection.
The paper tackles optical flow estimation under challenging conditions like illumination changes and occlusions by proposing a multi-scale generalized plane matching framework, achieving accurate occlusion localization and comparable flow quality to state-of-the-art methods on MPI-Sintel datasets.
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.