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Occlusions play an important role in disparity and optical flow estimation,since matching costs are not available in occluded areas and occlusionsindicate depth or motion boundaries. Moreover, occlusions are relevant formotion segmentation and scene flow estimation. In this paper, we present anefficient learning-based approach to estimate occlusion areas jointly withdisparities or optical flow. The estimated occlusions and motion boundariesclearly improve over the state-of-the-art. Moreover, we present networks withstate-of-the-art performance on the popular KITTI benchmark and good genericperformance. Making use of the estimated occlusions, we also show improvedresults on motion segmentation and scene flow estimation.

Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation