  As a flexible passive 3D sensing means, unsupervised learning of depth from
monocular videos is becoming an important research topic. It utilizes the
photometric errors between the target view and the synthesized views from its
adjacent source views as the loss instead of the difference from the ground
truth. Occlusion and scene dynamics in real-world scenes still adversely affect
the learning, despite significant progress made recently. In this paper, we
show that deliberately manipulating photometric errors can efficiently deal
with these difficulties better. We first propose an outlier masking technique
that considers the occluded or dynamic pixels as statistical outliers in the
photometric error map. With the outlier masking, the network learns the depth
of objects that move in the opposite direction to the camera more accurately.
To the best of our knowledge, such cases have not been seriously considered in
the previous works, even though they pose a high risk in applications like
autonomous driving. We also propose an efficient weighted multi-scale scheme to
reduce the artifacts in the predicted depth maps. Extensive experiments on the
KITTI dataset and additional experiments on the Cityscapes dataset have
verified the proposed approach's effectiveness on depth or ego-motion
estimation. Furthermore, for the first time, we evaluate the predicted depth on
the regions of dynamic objects and static background separately for both
supervised and unsupervised methods. The evaluation further verifies the
effectiveness of our proposed technical approach and provides some interesting
observations that might inspire future research in this direction.
