  Self-supervised monocular depth and ego-motion estimation is a promising
approach to replace or supplement expensive depth sensors such as LiDAR for
robotics applications like autonomous driving. However, most research in this
area focuses on a single monocular camera or stereo pairs that cover only a
fraction of the scene around the vehicle. In this work, we extend monocular
self-supervised depth and ego-motion estimation to large-baseline multi-camera
rigs. Using generalized spatio-temporal contexts, pose consistency constraints,
and carefully designed photometric loss masking, we learn a single network
generating dense, consistent, and scale-aware point clouds that cover the same
full surround 360 degree field of view as a typical LiDAR scanner. We also
propose a new scale-consistent evaluation metric more suitable to multi-camera
settings. Experiments on two challenging benchmarks illustrate the benefits of
our approach over strong baselines.

Computer Vision and Pattern Recognition