Visual Place Recognition (VPR) is a critical task in computer vision, traditionally enhanced by re-ranking retrieval results with image matching. However, recent advancements in VPR methods have significantly improved performance, challenging the necessity of re-ranking. In this work, we show that modern retrieval systems often reach a point where re-ranking can degrade results, as current VPR datasets are largely saturated. We propose using image matching as a verification step to assess retrieval confidence, demonstrating that inlier counts can reliably predict when re-ranking is beneficial. Our findings shift the paradigm of retrieval pipelines, offering insights for more robust and adaptive VPR systems. The code is available atthis https URL.
View on arXiv@article{sferrazza2025_2504.06116,
title={ To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition },
author={ Davide Sferrazza and Gabriele Berton and Gabriele Trivigno and Carlo Masone },
journal={arXiv preprint arXiv:2504.06116},
year={ 2025 }
}