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 at https://github.com/FarInHeight/To-Match-or-Not-to-Match.
@article{arxiv.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}
}