Quantifying Epistemic Uncertainty in Absolute Pose Regression
Journal:
arXiv
Published Date:
Apr 9, 2025
Abstract
Visual relocalization is the task of estimating the camera pose given an
image it views. Absolute pose regression offers a solution to this task by
training a neural network, directly regressing the camera pose from image
features. While an attractive solution in terms of memory and compute
efficiency, absolute pose regression's predictions are inaccurate and
unreliable outside the training domain. In this work, we propose a novel method
for quantifying the epistemic uncertainty of an absolute pose regression model
by estimating the likelihood of observations within a variational framework.
Beyond providing a measure of confidence in predictions, our approach offers a
unified model that also handles observation ambiguities, probabilistically
localizing the camera in the presence of repetitive structures. Our method
outperforms existing approaches in capturing the relation between uncertainty
and prediction error.
