Title: Digging Into Self-Supervised Monocular Depth Estimation,
Abstract: Depth-sensing is important for both navigation and scene understanding.
However, procuring RGB images with corresponding depth data for training deep
models is challenging; large-scale, varied, datasets with ground truth training
data are scarce. Consequently, several recent methods have proposed treating
the training of monocular color-to-depth estimation networks as an image
reconstruction problem, thus forgoing the need for ground truth depth.
There are multiple concepts and design decisions for these networks that seem
sensible, but give mixed or surprising results when tested. For example,
binocular stereo as the source of self-supervision seems cumbersome and hard to
scale, yet results are less blurry compared to training with monocular videos.
Such decisions also interplay with questions about architectures, loss
functions, image scales, and motion handling. In this paper, we propose a
simple yet effective model, with several general architectural and loss
innovations, that surpasses all other self-supervised depth estimation
approaches on KITTI.