To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition

• URL: http://arxiv.org/abs/2504.06116v2
• Date: Tue, 22 Apr 2025 07:44:36 GMT
• Title: To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
• Authors: Davide Sferrazza, Gabriele Berton, Gabriele Trivigno, Carlo Masone,
• Abstract summary: We show that modern retrieval systems often reach a point where re-ranking can degrade results, as current VPR datasets are largely saturated.<n>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.
• Score: 4.008780119020479
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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