MegaLoc: One Retrieval to Place Them All
Authors: name: Gabriele Berton, name: Carlo Masone

Retrieving images from the same location as a given query is an important
component of multiple computer vision tasks, like Visual Place Recognition,
Landmark Retrieval, Visual Localization, 3D reconstruction, and SLAM. However,
existing solutions are built to specifically work for one of these tasks, and
are known to fail when the requirements slightly change or when they meet
out-of-distribution data. In this paper we combine a variety of existing
methods, training techniques, and datasets to train a retrieval model, called
MegaLoc, that is performant on multiple tasks. We find that MegaLoc (1)
achieves state of the art on a large number of Visual Place Recognition
datasets, (2) impressive results on common Landmark Retrieval datasets, and (3)
sets a new state of the art for Visual Localization on the LaMAR datasets,
where we only changed the retrieval method to the existing localization
pipeline. The code for MegaLoc is available at
https://github.com/gmberton/MegaLoc