Abstract
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.
Abstract (translated)
遮挡在视差和光流估计中起重要作用，因为匹配成本在遮挡区域中不可用，遮挡表示深度或运动边界。此外，遮挡与运动分割和场景流估计相关。在本文中，我们提出了一种有效的基于学习的方法来估计遮挡区域与差异或光流量。估计的遮挡和运动边界明显改善了现有技术。此外，我们在流行的KITTI基准测试和良好的通用性能上呈现具有最先进性能的网络。利用估计的遮挡，我们还显示了运动分割和场景流估计的改进结果。
URL
https://arxiv.org/abs/1808.01838
