We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrin- sics from monocular videos, using only consistency across neighboring video frames as supervision signal. Similarly to prior work, our method learns by applying differen- tiable warping to frames and comparing the result to ad- jacent ones, but it provides several improvements: We ad- dress occlusions geometrically and differentiably, directly using the depth maps as predicted during training. We in- troduce randomized layer normalization, a novel power- ful regularizer, and we account for object motion relative to the scene. To the best of our knowledge, our work is the first to learn the camera intrinsic parameters, includ- ing lens distortion, from video in an unsupervised man- ner, thereby allowing us to extract accurate depth and motion from arbitrary videos of unknown origin at scale. We evaluate our results on the Cityscapes, KITTI and Eu- RoC datasets, establishing new state of the art on depth prediction and odometry, and demonstrate qualitatively that depth prediction can be learned from a collection of YouTube videos.
