  Majority of state-of-the-art monocular depth estimation methods are
supervised learning approaches. The success of such approaches heavily depends
on the high-quality depth labels which are expensive to obtain. Some recent
methods try to learn depth networks by leveraging unsupervised cues from
monocular videos which are easier to acquire but less reliable. In this paper,
we propose to resolve this dilemma by transferring knowledge from synthetic
videos with easily obtainable ground-truth depth labels. Due to the stylish
difference between synthetic and real images, we propose a
temporally-consistent domain adaptation (TCDA) approach that simultaneously
explores labels in the synthetic domain and temporal constraints in the videos
to improve style transfer and depth prediction. Furthermore, we make use of the
ground-truth optical flow and pose information in the synthetic data to learn
moving mask and pose prediction networks. The learned moving masks can filter
out moving regions that produces erroneous temporal constraints and the
estimated poses provide better initializations for estimating temporal
constraints. Experimental results demonstrate the effectiveness of our method
and comparable performance against state-of-the-art.
