  We propose FocusTune, a focus-guided sampling technique to improve the
performance of visual localization algorithms. FocusTune directs a scene
coordinate regression model towards regions critical for 3D point triangulation
by exploiting key geometric constraints. Specifically, rather than uniformly
sampling points across the image for training the scene coordinate regression
model, we instead re-project 3D scene coordinates onto the 2D image plane and
sample within a local neighborhood of the re-projected points. While our
proposed sampling strategy is generally applicable, we showcase FocusTune by
integrating it with the recently introduced Accelerated Coordinate Encoding
(ACE) model. Our results demonstrate that FocusTune both improves or matches
state-of-the-art performance whilst keeping ACE's appealing low storage and
compute requirements, for example reducing translation error from 25 to 19 and
17 to 15 cm for single and ensemble models, respectively, on the Cambridge
Landmarks dataset. This combination of high performance and low compute and
storage requirements is particularly promising for applications in areas like
mobile robotics and augmented reality. We made our code available at
\url{https://github.com/sontung/focus-tune}.
