  The self-supervised learning of depth and pose from monocular sequences
provides an attractive solution by using the photometric consistency of nearby
frames as it depends much less on the ground-truth data. In this paper, we
address the issue when previous assumptions of the self-supervised approaches
are violated due to the dynamic nature of real-world scenes. Different from
handling the noise as uncertainty, our key idea is to incorporate more robust
geometric quantities and enforce internal consistency in the temporal image
sequence. As demonstrated on commonly used benchmark datasets, the proposed
method substantially improves the state-of-the-art methods on both depth and
relative pose estimation for monocular image sequences, without adding
inference overhead.
