UniPR-3D: Towards Universal Visual Place Recognition with Visual Geometry Grounded Transformer

Visual Place Recognition (VPR) has been traditionally for-mulated as a single-image retrieval task. Using multiple views offers clearadvantages, yet this setting remains relatively underexplored and existingmethods often struggle to generalize across diverse environments. In thiswork, we introduce UniPR-3D, the first VPR architecture that effectivelyintegrates geometry-aware information from multiple views. UniPR-3Dbuilds on a VGGT backbone capable of encoding multi-view 3D represen-tations, which we adapt by designing feature aggregators and fine-tuningthem for the place recognition task. To construct our descriptor, wejointly leverage VGGT’s 3D tokens, but also intermediate 2D ones. Basedon their distinct characteristics, we design dedicated aggregation modulesfor 2D and 3D features, allowing our descriptor to capture fine-grainedtexture patterns while also reasoning across viewpoints. To further en-hance generalization, we incorporate both single- and multi-frame aggre-gation schemes, along with a variable-length sequence retrieval strategy.Our experiments show that UniPR-3D sets a new state of the art, out-performing both single- and multi-view baselines and highlighting theeffectiveness of geometry-grounded tokens for VPR. Our code and mod-els will be made publicly available on Github. We will release the codeand datasets publicly on https://github.com/dtc111111/UniPR-3D.