Digging Into Self-Supervised Monocular Depth Estimation

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.