Beyond 2D Matching: A Unified Single-Stage Framework for Geometry-Aware Cross-View Object Geo-Localization

Cross-view object geo-localization (CVOGL) aims to locatea target object from a query view (e.g., ground or drone) within ageo-tagged reference image (e.g., satellite). Existing approaches heav-ily rely on 2D appearance matching and are constrained by limiteddatasets lacking geometric metadata, diverse prompts, and standardfield-of-view imagery. To address these intertwined challenges, we firstintroduce CMA-Loc, a large-scale, high-fidelity building dataset compris-ing over 220,000 ground-satellite and drone-satellite pairs. It providesmulti-modal prompts (points, boxes, masks) and camera poses to en-able flexible target referring and explicit spatial modeling. Furthermore,we propose a novel single-stage Geometry-Aware Geo-localization frame-work (GAGeo), built upon the permutation-equivariant 3D foundationmodel π 3 . By seamlessly integrating visual features, referring prompts,and learnable task tokens, our model adapts the inherited 3D prior tojointly predict bounding boxes, segmentation masks, and camera posesin a single forward pass. Additionally, we introduce a contrastive lossthat utilizes the satellite view as a universal anchor, implicitly aligningground and drone representations to enable zero-shot ground-to-drone lo-calization without requiring triplet training data. Extensive experimentsdemonstrate that our approach significantly outperforms state-of-the-artmethods, exhibiting exceptional generalization ability in unseen scenesand novel cross-view setups.