From Topology to Metric: Geometry-Grounded Refinement for Metric Place Recognition

Visual Place Recognition (VPR) aims to localize query images by retrieving visually matched geo-tagged database images. Existing works mainly achieve Topological Place Recognition, where the coordinate of the retrieved database image is directly assigned to the query location, overlooking the spatial offset between matched images and only getting the coarse geographical location. To address this issue, we introduce Metric Place Recognition and propose GeoMPR, a geometry-grounded framework that extends topological retrieval toward precise geographic position estimation. GeoMPR first retrieves a top-$k$ set of geo-tagged candidates and filters the candidates to form a locally coherent reference set. Given the query and these references, VGGT estimates their relative camera geometry. The Spatial Offset Estimation Module (SOEP) first establishes the transformation mapping between the predicted relative camera geometry and the spatial offset using these reference images, and subsequently leverages this mapping to derive the spatial offset estimation of the query image relative to the anchor, and refines the estimation using residual compensation and global correction. Moreover, we introduce a Geometry-aware Spatial Offset Loss to explicitly supervise the spatial displacement between query and anchor images for accurate geographic position estimation. Extensive experiments on several challenging VPR benchmarks demonstrate that GeoMPR substantially improves place recognition accuracy under strict distance thresholds. Notably, GeoMPR improves the 5-meter recognition accuracy of the SOTA method on Tokyo24/7 by 42.5 pp (36.8% $\to$ 79.3%), demonstrating the effectiveness of geometry-grounded refinement. Codes will be publicly available.