  We address the problem of depth and ego-motion estimation from image
sequences. Recent advances in the domain propose to train a deep learning model
for both tasks using image reconstruction in a self-supervised manner. We
revise the assumptions and the limitations of the current approaches and
propose two improvements to boost the performance of the depth and ego-motion
estimation. We first use Lie group properties to enforce the geometric
consistency between images in the sequence and their reconstructions. We then
propose a mechanism to pay an attention to image regions where the image
reconstruction get corrupted. We show how to integrate the attention mechanism
in the form of attention gates in the pipeline and use attention coefficients
as a mask. We evaluate the new architecture on the KITTI datasets and compare
it to the previous techniques. We show that our approach improves the
state-of-the-art results for ego-motion estimation and achieve comparable
results for depth estimation.
