论文标题
OccInpflow：通过无监督学习估算闭塞光流量
OccInpFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning
论文作者
论文摘要
在无监督的光流学习中，闭塞是一个不可避免的关键问题。现有方法要么将闭塞视为非封闭区域，要么简单地将其删除以避免错误。但是，遮挡区域可以为光流学习提供有效的信息。在本文中，我们提出了OccInpflow，这是一个充分利用遮挡区域的闭塞框架。具体而言，提出了一个新的外观流网络，以根据图像内容进行封闭的流量。此外，提出了边界经扭曲来处理由图像边界以外的位移引起的遮挡。我们在多个领先的流基准数据集（例如飞行椅，Kitti和MPI-Sintel）上进行实验，这表明我们提出的封闭式处理框架可以显着提高性能。
Occlusion is an inevitable and critical problem in unsupervised optical flow learning. Existing methods either treat occlusions equally as non-occluded regions or simply remove them to avoid incorrectness. However, the occlusion regions can provide effective information for optical flow learning. In this paper, we present OccInpFlow, an occlusion-inpainting framework to make full use of occlusion regions. Specifically, a new appearance-flow network is proposed to inpaint occluded flows based on the image content. Moreover, a boundary warp is proposed to deal with occlusions caused by displacement beyond image border. We conduct experiments on multiple leading flow benchmark data sets such as Flying Chairs, KITTI and MPI-Sintel, which demonstrate that the performance is significantly improved by our proposed occlusion handling framework.
