  Can we relocalize in a scene represented by a single reference image?
Standard visual relocalization requires hundreds of images and scale
calibration to build a scene-specific 3D map. In contrast, we propose Map-free
Relocalization, i.e., using only one photo of a scene to enable instant, metric
scaled relocalization. Existing datasets are not suitable to benchmark map-free
relocalization, due to their focus on large scenes or their limited
variability. Thus, we have constructed a new dataset of 655 small places of
interest, such as sculptures, murals and fountains, collected worldwide. Each
place comes with a reference image to serve as a relocalization anchor, and
dozens of query images with known, metric camera poses. The dataset features
changing conditions, stark viewpoint changes, high variability across places,
and queries with low to no visual overlap with the reference image. We identify
two viable families of existing methods to provide baseline results: relative
pose regression, and feature matching combined with single-image depth
prediction. While these methods show reasonable performance on some favorable
scenes in our dataset, map-free relocalization proves to be a challenge that
requires new, innovative solutions.
