MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable
  Uncertainty
Rémi Marsal Florian Chabot Angelique Loesch William Grolleau Hichem Sahbi
Self-supervised monocular depth estimation methods aim to be used in critical
applications such as autonomous vehicles for environment analysis. To
circumvent the potential imperfections of these approaches, a quantification of
the prediction confidence is crucial to guide decision-making systems that rely
on depth estimation. In this paper, we propose MonoProb, a new unsupervised
monocular depth estimation method that returns an interpretable uncertainty,
which means that the uncertainty reflects the expected error of the network in
its depth predictions. We rethink the stereo or the structure-from-motion
paradigms used to train unsupervised monocular depth models as a probabilistic
problem. Within a single forward pass inference, this model provides a depth
prediction and a measure of its confidence, without increasing the inference
time. We then improve the performance on depth and uncertainty with a novel
self-distillation loss for which a student is supervised by a pseudo ground
truth that is a probability distribution on depth output by a teacher. To
quantify the performance of our models we design new metrics that, unlike
traditional ones, measure the absolute performance of uncertainty predictions.
Our experiments highlight enhancements achieved by our method on standard depth
and uncertainty metrics as well as on our tailored metrics.
https://github.com/CEA-LIST/MonoProb