  Scene coordinate regression (SCR) methods are a family of visual localization
methods that directly regress 2D-3D matches for camera pose estimation. They
are effective in small-scale scenes but face significant challenges in
large-scale scenes that are further amplified in the absence of ground truth 3D
point clouds for supervision. Here, the model can only rely on reprojection
constraints and needs to implicitly triangulate the points. The challenges stem
from a fundamental dilemma: The network has to be invariant to observations of
the same landmark at different viewpoints and lighting conditions, etc., but at
the same time discriminate unrelated but similar observations. The latter
becomes more relevant and severe in larger scenes. In this work, we tackle this
problem by introducing the concept of co-visibility to the network. We propose
GLACE, which integrates pre-trained global and local encodings and enables SCR
to scale to large scenes with only a single small-sized network. Specifically,
we propose a novel feature diffusion technique that implicitly groups the
reprojection constraints with co-visibility and avoids overfitting to trivial
solutions. Additionally, our position decoder parameterizes the output
positions for large-scale scenes more effectively. Without using 3D models or
depth maps for supervision, our method achieves state-of-the-art results on
large-scale scenes with a low-map-size model. On Cambridge landmarks, with a
single model, we achieve 17% lower median position error than Poker, the
ensemble variant of the state-of-the-art SCR method ACE. Code is available at:
https://github.com/cvg/glace.
