Self-Supervised Monocular Depth Estimation via Discrete Strategy and Uncertainty

Zhenyu Li, Junjun Jiang, Xianming Liu

Abstract: Dear Editor, This letter is concerned with self-supervised monocular depth estimation. To estimate uncertainty simultaneously, we propose a simple yet effective strategy to learn the uncertainty for self-supervised monocular depth estimation with the discrete strategy that explicitly associates the prediction and the uncertainty to train the networks. Furthermore, we propose the uncertainty-guided feature fusion module to fully utilize the uncertainty information. Codes will be available at https://github.com/zhyever/Monocular-Depth-Esti-mation-Toolbox.