摘要： Efficient optical flow estimation with high accuracy is a challenging problem in computer vision. In this paper, we present a simple but efficient segmentation-based PatchMatch framework to address this issue. Specifically, it firstly generates sparse seeds without losing important motion information by oversegmentation, and then yields sparse matches by adopting a coarse-to-fine PatchMatch with sparse seeds. Such a scheme enhances the robustness of global regularization and yields better matching results compared with the existing NNF techniques while leading to a significant speed-up due to the sparsity of these seeds. Simultaneously, we introduce an extended nonlocal propagation and adaptive random search to address the basic limitation of traditional coarse-to-fine framework in handing motion details that often vanish at coarser levels. Finally, we obtain dense matches at the finest level through an efficient sparse-to-dense matching according to the cues of oversegmentation. While performing an efficient approximation for oversegmentation, the proposed algorithm runs significantly fast and are robust to large displacements while preserving important motion details. It also achieves good performance on the challenging MPI-Sintel and Kitti flow 2015 datasets.