Revisiting gradient-based uncertainty for monocular depth estimation

Julia Hornauer, Amir El-Ghoussani, Vasileios Belagiannis

Abstract:

Monocular depth estimation, similar to other image
based tasks, is prone to erroneous predictions due to ambiguities
in the image, for example, caused by dynamic objects or shadows.
For this reason, pixel-wise uncertainty assessment is required for
safety-critical applications to highlight the areas where the predic
tion is unreliable. We address this in a post hoc manner and in
troduce gradient-based uncertainty estimation for already trained
depth estimation models. To extract gradients without depending
on the ground truth depth, we introduce an auxiliary loss function
based on the consistency of the predicted depth and a reference
depth. The reference depth, which acts as pseudo ground truth, is
in fact generated using a simple image or feature augmentation,
making our approach simple and effective. To obtain the final
uncertainty score, the derivatives w.r.t. the feature maps from
single or multiple layers are calculated using back-propagation.
We demonstrate that our gradient-based approach is effective in
determiningtheuncertaintywithoutre-trainingusingthetwostan
darddepthestimationbenchmarksKITTIandNYU.Inparticular,
for models trained with monocular sequences and therefore most
prone to uncertainty, our method outperforms related approaches.