What type of study is this?

This is a Quantitative Study study.

October 16, 2025Open Access

MegaLoc: One Retrieval to Place Them All

Key Points

MegaLoc demonstrates state of the art performance in visual place recognition across various datasets, enhancing recognition capabilities.
The model achieves impressive results in landmark retrieval, underscoring its adaptability in different contexts for image retrieval.
By only altering the retrieval method, MegaLoc sets new benchmarks in visual localization on the LaMAR datasets, showcasing its effectiveness.
The approach effectively combines existing methods and datasets, indicating potential applicability to out-of-distribution data challenges.

Abstract

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

Read Full Paperexternally

اسأل الذكاء الاصطناعي

Bookmark

View Full Paper