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This paper presents a semantic-guided interpolation scheme (SemFlow) to handle motion boundaries and occlusions in large displacement optical flow. The basic idea is to segment images into superpixels and estimate their homographies for interpolation. In order to ensure each superpixel can be approximated as a plane, a semantic-guided refinement method is introduced. Moreover, we put forward a homography estimation model weighted by the distance between each superpixel and its K-nearest neighbors. Our newly-proposed distance metric combines the texture and semantic information to find proper neighbors. Our homography model performs better than the original affine model, since it accords with the real world projection relationship. The experiments on KITTI dataset demonstrate that SemFlow outperforms other state-of-the-art methods, especially in solving the problem of large scale motions and occlusions.

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