  Although both self-supervised single-frame and multi-frame depth estimation
methods only require unlabeled monocular videos for training, the information
they leverage varies because single-frame methods mainly rely on
appearance-based features while multi-frame methods focus on geometric cues.
Considering the complementary information of single-frame and multi-frame
methods, some works attempt to leverage single-frame depth to improve
multi-frame depth. However, these methods can neither exploit the difference
between single-frame depth and multi-frame depth to improve multi-frame depth
nor leverage multi-frame depth to optimize single-frame depth models. To fully
utilize the mutual influence between single-frame and multi-frame methods, we
propose a novel self-supervised training framework. Specifically, we first
introduce a pixel-wise adaptive depth sampling module guided by single-frame
depth to train the multi-frame model. Then, we leverage the minimum
reprojection based distillation loss to transfer the knowledge from the
multi-frame depth network to the single-frame network to improve single-frame
depth. Finally, we regard the improved single-frame depth as a prior to further
boost the performance of multi-frame depth estimation. Experimental results on
the KITTI and Cityscapes datasets show that our method outperforms existing
approaches in the self-supervised monocular setting.
