Uncertainty Quantification in Depth Estimation via Constrained Ordinal Regression

Abstract

Monocular Depth Estimation (MDE) is a task to predict a dense depth map from a single image. Despite the recent progress brought by deep learning, existing methods are still prone to errors due to the ill-posed nature of MDE. Hence depth estimation systems must be self-aware of possible mistakes to avoid disastrous consequences. This paper provides an uncertainty quantification method for supervised MDE models. From a frequentist view, we capture the uncertainty by predictive variance that consists of two terms: error variance and estimation variance. The former represents the noise of a depth value, and the latter measures the randomness in the depth regression model due to training on fini..