arXiv:1904.04998cs.CV
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
Abstract
We present a novel method for simultaneous learning of depth, egomotion, object motion, and camera intrinsics from monocular videos, using only consistency across neighboring video frames as supervision signal. Similarly to prior work, our method learns by applying differentiable warping to frames and comparing the result to adjacent ones, but it provides several improvements: We address occlusions geometrically and differentiably, directly using the depth maps as predicted during training. We introduce randomized layer normalization, a novel powerful 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, including lens distortion, from video in an unsupervised manner, 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 EuRoC 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.
I strongly disagree! I have seen fantastic results with stereo cameras, colored lights, and self-calibration. For most use cases, it's no problem if your robot will stop for a few seconds, rotate the camera axis around a bit, and then continue. But that appears to be good enough…
fxtentacleView on HNParent context