Self-Supervised Monocular Depth Estimation via Discrete Strategy and Uncertainty

摘要

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-Estimation-Toolbox.Self-supervised monocular depth estimation methods turn into promising alternative trade-offs in both the training cost and the inference performance.However,compound losses that couple the depth and the pose lead to a dilemma of uncertainty calculation that is crucial for critical safety systems.To solve this issue,we propose a simple yet effective strategy to learn the uncertainty for self-supervised monocular depth estimation using the discrete bins that explicitly associate the prediction and the uncertainty to train the networks.This strategy is more pluggable without any additional changes to self-supervised training losses and improves model performance.Secondly,to further exert the uncertainty information,we propose the uncertainty-guided feature fusion module to refine the depth estimation.