  Occlusions play an important role in disparity and optical flow estimation,
since matching costs are not available in occluded areas and occlusions
indicate depth or motion boundaries. Moreover, occlusions are relevant for
motion segmentation and scene flow estimation. In this paper, we present an
efficient learning-based approach to estimate occlusion areas jointly with
disparities or optical flow. The estimated occlusions and motion boundaries
clearly improve over the state-of-the-art. Moreover, we present networks with
state-of-the-art performance on the popular KITTI benchmark and good generic
performance. Making use of the estimated occlusions, we also show improved
results on motion segmentation and scene flow estimation.
