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MegaLoc: One Retrieval to Place Them All

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arxiv 2502.17237 v3 pith:LPIVVIML submitted 2025-02-24 cs.CV

Gabriele Berton , Carlo Masone This is my paper
classification cs.CV
keywords megalocretrievaldatasetsvisualexistinglocalizationplacetasks
verification ladder T0 review T1 audit T2 compute T3 formal
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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

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset

    cs.CV 2025-06 conditional novelty 6.0 of 10

    This paper releases and benchmarks a 30 km egocentric day-and-night dataset with SLAM poses and TLS ground truth, and shows current NVS and relocalization methods degrade sharply at night.

  2. Hi^2-GSLoc: Dual-Hierarchical Gaussian-Specific Visual Relocalization for Remote Sensing

    cs.CV 2025-07 reject novelty 5.0 of 10

    A 3DGS-based aerial visual relocalization pipeline reports strong accuracy, but its 100 percent recall after filtering is achieved by excluding hard queries from the evaluation.

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