Coarse-to-Fine PatchMatch for Dense Correspondence

Abstract

Although the matching technique has been studied in various areas of computer vision for decades, efficient dense correspondence remains an open problem. In this paper, we present a simple but powerful matching method that works in a coarse-to-fine scheme for optical flow and stereo matching. Inspired by the nearest neighbor field (NNF) algorithms, our approach, called coarse-to-fine PatchMatch, blends an efficient random search strategy with the coarse-to-fine scheme for efficient dense correspondence. Unlike existing NNF techniques, which are efficient but yield results that are often too noisy because of a lack of global regularization, we propose a propagation step involving a constrained random search radius between adjacent levels of a hierarchical architecture. The resulting correspondence has a built-in smoothing effect, making it more suited to dense correspondence than the NNF techniques. Furthermore, our approach can also capture tiny structures with large motions, which is a problem for traditional coarse-to-fine methods. Interpolated using an edge-preserving interpolation method, our method outperforms the state-of-the-art optical flow methods on the MPI-Sintel and KITTI data sets and is much faster than competing methods.