  Absolute Pose Regressors (APRs) directly estimate camera poses from monocular
images, but their accuracy is unstable for different queries. Uncertainty-aware
APRs provide uncertainty information on the estimated pose, alleviating the
impact of these unreliable predictions. However, existing uncertainty modelling
techniques are often coupled with a specific APR architecture, resulting in
suboptimal performance compared to state-of-the-art (SOTA) APR methods. This
work introduces a novel APR-agnostic framework, HR-APR, that formulates
uncertainty estimation as cosine similarity estimation between the query and
database features. It does not rely on or affect APR network architecture,
which is flexible and computationally efficient. In addition, we take advantage
of the uncertainty for pose refinement to enhance the performance of APR. The
extensive experiments demonstrate the effectiveness of our framework, reducing
27.4\% and 15.2\% of computational overhead on the 7Scenes and Cambridge
Landmarks datasets while maintaining the SOTA accuracy in single-image APRs.
