To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912340112310272 |
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| author | Sferrazza, Davide Berton, Gabriele Trivigno, Gabriele Masone, Carlo |
| author_facet | Sferrazza, Davide Berton, Gabriele Trivigno, Gabriele Masone, Carlo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_06116 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition Sferrazza, Davide Berton, Gabriele Trivigno, Gabriele Masone, Carlo Computer Vision and Pattern Recognition 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. |
| title | To Match or Not to Match: Revisiting Image Matching for Reliable Visual Place Recognition |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.06116 |