Uncertainty-Aware Self-Improving Framework for Depth Estimation

Abstract

In recent years, self-supervised paradigms for depth estimation have drawn lots of attention from the community. Promising as they are, in order to achieve wider application and better performance, reasoning about the uncertainty of the estimated depth maps is in great demand. In this paper, we propose a novel uncertainty-aware self-improving framework for self-supervised depth estimation, which could model and exploit uncertainty to boost depth accuracy without any external supervision. In detail, our framework includes two DepthNets–a teacher network and a student network. The teacher network is pre-trained in a self-supervised manner and the predicted depth is leveraged as pseudo-ground-truth to supervise the learning of the student network, leading to better estimation accuracy and the ability of modeling depth uncertainty. Afterward, the estimated uncertainty would be introduced back into the teacher network as the mask, preventing erroneous regions from contributing to network training and depth estimation. In this way, a better pseudo-ground-truth from the teacher network will be used for the next iteration of the student network training. We evaluate our proposed framework on the widely used KITTI dataset and TUM RGB-D dataset with both quantitative and qualitative experiments. The experimental results show that our framework achieves quite satisfactory performance among the self-supervised depth estimation methods. Extensive experiments which combine the student network and feature-based monocular SLAM system (i.e ORB-SLAM3) further demonstrate the benefits of the introduced depth uncertainty.