title: MegaLoc: One Retrieval to Place Them All
summary: 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
paper_authors._id: 67bdcb947186ab0e92d9ebf6
paper_authors.name: Gabriele Berton
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-02-25T13:54:29.302Z
paper_authors._id: 67a9e16f0710558f7bd8947a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/2SacRuI2bOsBgctaxWNGl.png
paper_authors.fullname: Gabriele Berton
paper_authors.type: user
paper_authors.user: gberton
paper_authors._id: 67bdcb947186ab0e92d9ebf7
paper_authors.name: Carlo Masone
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-25T16:40:30.511Z
paper_authors._id: 643fb774e5f6d513c7214ec3
paper_authors.avatarUrl: /avatars/9333c175e72ced43d158ecd3a40c6af4.svg
paper_authors.fullname: Carlo Masone
paper_authors.type: user
paper_authors.user: carmas
paper_title: MegaLoc: One Retrieval to Place Them All
paper_summary: 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
paper_ai_summary: 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.
paper_ai_keywords: visual place recognition landmark retrieval visual localization 3D reconstruction SLAM retrieval method LaMAR datasets