UniPS: Unified Patch Similarity Unlocks Training-Free Visual Place Recognition

Visual Place Recognition (VPR) localises an image by retrieving the geo- referenced database image that shows the same place. Most VPR methods are training-based, aiming to learn effective features from annotated data. These methods achieve strong results on standard VPR benchmarks but require millions of labelled samples, making annotation cost a significant challenge. Even with such extensive training, they still fail on other VPR benchmarks that differ from their training set, such as cross-environment benchmarks. Training-free VPR methods avoid this annotation cost and generalise better. However, these methods do not fully leverage pretrained vision models, resulting in features that lack sufficient discriminative power to achieve strong performance on standard VPR benchmarks, highlighting the challenge of effectively utilising pretrained vision models. To tackle these challenges, we propose UniPS (Unified Patch Similarity), a training-free pipeline built solely on patch similarity. Given a query or database image, only a frozen backbone is applied to extract patch features. We then perform a series of patch similarity-based comparisons for VPR: self-similarity for patch importance, followed by weighted second-moment patch feature pooling for coarse ranking, and cross-image patch similarity for reranking. Based on comprehensive evaluation on 17 benchmarks, UniPS achieves state-of-the-art results on both the 8 standard and 9 cross-environment VPR benchmarks. On standard VPR benchmarks, UniPS outperforms the best training-free baseline by +12.9% and the best trained baseline by +0.2%, despite the latter’s heavy annotation requirements. On cross-environment benchmarks, UniPS outperforms the best training-free baseline by +7.1% and the best trained baseline by +11.5%. In addition, the proposed module can be easily plugged into existing methods, improving their performance by +5.6% to up to +17.8%.