  Unsupervised learning of depth and ego-motion from unlabelled monocular
videos has recently drawn great attention, which avoids the use of expensive
ground truth in the supervised one. It achieves this by using the photometric
errors between the target view and the synthesized views from its adjacent
source views as the loss. Despite significant progress, the learning still
suffers from occlusion and scene dynamics. This paper shows that carefully
manipulating photometric errors can tackle these difficulties better. The
primary improvement is achieved by a statistical technique that can mask out
the invisible or nonstationary pixels in the photometric error map and thus
prevents misleading the networks. With this outlier masking approach, the depth
of objects moving in the opposite direction to the camera can be estimated more
accurately. To the best of our knowledge, such scenarios have not been
seriously considered in the previous works, even though they pose a higher 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 show the effectiveness of the
proposed approaches. The overall system achieves state-of-theart performance on
both depth and ego-motion estimation.
