3 research outputs found
Gradient-based Uncertainty for Monocular Depth Estimation
In monocular depth estimation, disturbances in the image context, like moving
objects or reflecting materials, can easily lead to erroneous predictions. For
that reason, uncertainty estimates for each pixel are necessary, in particular
for safety-critical applications such as automated driving. We propose a post
hoc uncertainty estimation approach for an already trained and thus fixed depth
estimation model, represented by a deep neural network. The uncertainty is
estimated with the gradients which are extracted with an auxiliary loss
function. To avoid relying on ground-truth information for the loss definition,
we present an auxiliary loss function based on the correspondence of the depth
prediction for an image and its horizontally flipped counterpart. Our approach
achieves state-of-the-art uncertainty estimation results on the KITTI and NYU
Depth V2 benchmarks without the need to retrain the neural network. Models and
code are publicly available at https://github.com/jhornauer/GrUMoDepth.Comment: Accepted to ECCV 202