MegaLoc: One Retrieval to Place Them All
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
67bdcb947186ab0e92d9ebf6
Gabriele Berton
extracted_pending
2025-02-25T13:54:29.302Z
67a9e16f0710558f7bd8947a
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2SacRuI2bOsBgctaxWNGl.png
Gabriele Berton
user
gberton
67bdcb947186ab0e92d9ebf7
Carlo Masone
admin_assigned
2025-02-25T16:40:30.511Z
643fb774e5f6d513c7214ec3
/avatars/9333c175e72ced43d158ecd3a40c6af4.svg
Carlo Masone
user
carmas
MegaLoc: One Retrieval to Place Them All
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
MegaLoc, a combination of various methods and techniques, achieves top performance across multiple computer vision tasks including visual place recognition, landmark retrieval, and visual localization.
visual place recognition landmark retrieval visual localization 3D reconstruction SLAM retrieval method LaMAR datasets