Variational Monocular Depth Estimation for Reliability Prediction

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