  Visual re-localization means using a single image as input to estimate the
camera's location and orientation relative to a pre-recorded environment. The
highest-scoring methods are "structure based," and need the query camera's
intrinsics as an input to the model, with careful geometric optimization. When
intrinsics are absent, methods vie for accuracy by making various other
assumptions. This yields fairly good localization scores, but the models are
"narrow" in some way, eg., requiring costly test-time computations, or depth
sensors, or multiple query frames. In contrast, our proposed method makes few
special assumptions, and is fairly lightweight in training and testing.
  Our pose regression network learns from only relative poses of training
scenes. For inference, it builds a graph connecting the query image to training
counterparts and uses a graph neural network (GNN) with image representations
on nodes and image-pair representations on edges. By efficiently passing
messages between them, both representation types are refined to produce a
consistent camera pose estimate. We validate the effectiveness of our approach
on both standard indoor (7-Scenes) and outdoor (Cambridge Landmarks) camera
re-localization benchmarks. Our relative pose regression method matches the
accuracy of absolute pose regression networks, while retaining the
relative-pose models' test-time speed and ability to generalize to non-training
scenes.
