Measuring and Modeling Uncertainty Degree for Monocular Depth Estimation
Mochu Xiang Jing Zhang Nick Barnes Yuchao Dai
Effectively measuring and modeling the reliability of a trained model is
essential to the real-world deployment of monocular depth estimation (MDE)
models. However, the intrinsic ill-posedness and ordinal-sensitive nature of
MDE pose major challenges to the estimation of uncertainty degree of the
trained models. On the one hand, utilizing current uncertainty modeling methods
may increase memory consumption and are usually time-consuming. On the other
hand, measuring the uncertainty based on model accuracy can also be
problematic, where uncertainty reliability and prediction accuracy are not well
decoupled. In this paper, we propose to model the uncertainty of MDE models
from the perspective of the inherent probability distributions originating from
the depth probability volume and its extensions, and to assess it more fairly
with more comprehensive metrics. By simply introducing additional training
regularization terms, our model, with surprisingly simple formations and
without requiring extra modules or multiple inferences, can provide uncertainty
estimations with state-of-the-art reliability, and can be further improved when
combined with ensemble or sampling methods. A series of experiments demonstrate
the effectiveness of our methods.