  Self-supervised learning of depth map prediction and motion estimation from
monocular video sequences is of vital importance -- since it realizes a broad
range of tasks in robotics and autonomous vehicles. A large number of research
efforts have enhanced the performance by tackling illumination variation,
occlusions, and dynamic objects, to name a few. However, each of those efforts
targets individual goals and endures as separate works. Moreover, most of
previous works have adopted the same CNN architecture, not reaping
architectural benefits. Therefore, the need to investigate the inter-dependency
of the previous methods and the effect of architectural factors remains. To
achieve these objectives, we revisit numerous previously proposed
self-supervised methods for joint learning of depth and motion, perform a
comprehensive empirical study, and unveil multiple crucial insights.
Furthermore, we remarkably enhance the performance as a result of our study --
outperforming previous state-of-the-art performance.
