  The great potential of unsupervised monocular depth estimation has been
demonstrated by many works due to low annotation cost and impressive accuracy
comparable to supervised methods. To further improve the performance, recent
works mainly focus on designing more complex network structures and exploiting
extra supervised information, e.g., semantic segmentation. These methods
optimize the models by exploiting the reconstructed relationship between the
target and reference images in varying degrees. However, previous methods prove
that this image reconstruction optimization is prone to get trapped in local
minima. In this paper, our core idea is to guide the optimization with prior
knowledge from pretrained Flow-Net. And we show that the bottleneck of
unsupervised monocular depth estimation can be broken with our simple but
effective framework named FG-Depth. In particular, we propose (i) a flow
distillation loss to replace the typical photometric loss that limits the
capacity of the model and (ii) a prior flow based mask to remove invalid pixels
that bring the noise in training loss. Extensive experiments demonstrate the
effectiveness of each component, and our approach achieves state-of-the-art
results on both KITTI and NYU-Depth-v2 datasets.
