  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.
