  Learning depth and camera ego-motion from raw unlabeled RGB video streams is
seeing exciting progress through self-supervision from strong geometric cues.
To leverage not only appearance but also scene geometry, we propose a novel
self-supervised two-stream network using RGB and inferred depth information for
accurate visual odometry. In addition, we introduce a sparsity-inducing data
augmentation policy for ego-motion learning that effectively regularizes the
pose network to enable stronger generalization performance. As a result, we
show that our proposed two-stream pose network achieves state-of-the-art
results among learning-based methods on the KITTI odometry benchmark, and is
especially suited for self-supervision at scale. Our experiments on a
large-scale urban driving dataset of 1 million frames indicate that the
performance of our proposed architecture does indeed scale progressively with
more data.
