  Self-supervised monocular depth estimation has shown impressive results in
static scenes. It relies on the multi-view consistency assumption for training
networks, however, that is violated in dynamic object regions and occlusions.
Consequently, existing methods show poor accuracy in dynamic scenes, and the
estimated depth map is blurred at object boundaries because they are usually
occluded in other training views. In this paper, we propose SC-DepthV3 for
addressing the challenges. Specifically, we introduce an external pretrained
monocular depth estimation model for generating single-image depth prior,
namely pseudo-depth, based on which we propose novel losses to boost
self-supervised training. As a result, our model can predict sharp and accurate
depth maps, even when training from monocular videos of highly-dynamic scenes.
We demonstrate the significantly superior performance of our method over
previous methods on six challenging datasets, and we provide detailed ablation
studies for the proposed terms. Source code and data will be released at
https://github.com/JiawangBian/sc_depth_pl
