Variational Monocular Depth Estimation for Reliability Prediction


Noriaki Hirose, Shun Taguchi, Keisuke Kawano and Satoshi Koide


Self-supervised learning for monocular depth estimation has been widely investigated as an alternative to the supervised learning approach. Uncertainty estimation in depth estimation is a crucial problem for applications, such as autonomous driving, in detecting unreliable depth. In this study, we propose a variational model to estimate depth uncertainty in self-supervised learning. Our approach leverages time-series images to handle the depth distribution from appearance variations in training. We introduce the Mahalanobis--Wasserstein distance between two consecutive frames to learn the uncertainty. In inference, our method estimates the uncertainty map and the depth image at each pixel from a single image. In experiments on KITTI, Make3D, and DIODE datasets, we show that our model achieves better uncertainty estimation than previous approaches as well as high accuracy of depth estimation.

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  Important Dates

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Paper registration July 23 30, 2021
Paper submission July 30, 2021
Supplementary August 8, 2021
Tutorial submission August 15, 2021
Tutorial notification August 31, 2021
Rebuttal period September 16-22, 2021
Paper notification October 1, 2021
Camera ready October 15, 2021
Demo submission July 30 Nov 15, 2021
Demo notification Oct 1 Nov 19, 2021
Tutorial November 30, 2021
Main conference December 1-3, 2021