想搞跨视角目标定位？这篇论文用GAGeo和22万对数据集解决了2D匹配的局限，还能零样本从地面转到无人机视角。
现有跨视角目标地理定位方法依赖2D外观匹配，受限于缺少几何元数据的数据集。研究者提出GAGeo框架，基于置换等变3D基础模型π³，在单次前向传播中联合预测边界框、分割掩码和相机位姿。新构建的GeoTerra数据集包含超过22万对地面-卫星和无人机-卫星图像，提供多模态提示（点、框、蒙版）和相机位姿。引入的对比损失利用卫星视图作为通用锚点，实现零样本地面到无人机定位。实验表明该方法在未见场景和新型跨视角设置中显著优于现有方法。
Beyond 2D Matching: A Unified Single-Stage Framework for Geometry-Aware Cross-View Object Geo-Localization
Cross-view object geo-localization (CVOGL) aims to locate a target object from a query view (e.g., ground or drone) within a geo-tagged reference image (e.g., satellite). Existing approaches heavily rely on 2D appearance matching and are constrained by limited datasets lacking geometric metadata, diverse prompts, and standard field-of-view imagery. To address these intertwined challenges, we first introduce \dataset, a large-scale, high-fidelity building dataset comprising over 220,000 ground-satellite and drone-satellite pairs. It provides multi-modal prompts (points, boxes, masks) and camera poses to enable flexible target referring and explicit spatial modeling. Furthermore, we propose a novel single-stage Geometry-Aware Geo-localization framework (GAGeo), built upon the permutation-equivariant 3D foundation model $π^3$. By seamlessly integrating visual features, referring prompts, and learnable task tokens, our model adapts the inherited 3D prior to jointly predict bounding boxes, segmentation masks, and camera poses in a single forward pass. Additionally, we introduce a contrastive loss that utilizes the satellite view as a universal anchor, implicitly aligning ground and drone representations to enable zero-shot ground-to-drone localization without requiring triplet training data. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods, exhibiting exceptional generalization ability in unseen scenes and novel cross-view setups.