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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.

6D Object pose estimation is a fundamental component in robotics enabling efficient interaction with the environment. It is particularly challenging in bin-picking applications, where many objects are low-feature and reflective, and self-occlusion between objects of the same type is common. We propose a novel multi-view approach leveraging known camera transformations from an eye-in-hand setup to combine heatmap and keypoint estimates into a probability density map over 3D space. The result is a robust approach that is scalable in the number of views. It relies on a confidence score composed of keypoint probabilities and point-cloud alignment error, which allows reliable rejection of false positives. We demonstrate an average pose estimation error of approximately 0.5 mm and 2 degrees across a variety of difficult low-feature and reflective objects in the ROBI dataset, while also surpassing the stateof-art correct detection rate, measured using the 10% object diameter threshold on ADD error.