GeoLink：一种面向跨视角地理定位更好泛化的3D-aware框架
GeoLink: A 3D-aware Framework to Improve Generalization for Cross-view Geo-localization
AI总结：
本文提出GeoLink框架，通过3D感知的语义一致方法提升跨视角地理定位的泛化能力，实验表明其在多个基准测试中优于现有方法，能适应未见领域和多变天气。
摘要：
通用的跨视角地理定位旨在在未见区域和条件下匹配相同位置，其核心挑战在于视角变化导致的严重语义不一致和域移时的泛化能力差。现有方法主要依赖2D对应，但容易被视角间冗余共享信息干扰，导致转移性差。为此，我们提出GeoLink，一种3D感知的语义一致框架。具体而言，我们使用VGGT从多视角无人机图像离线重建场景点云，提供稳定的结构先验。基于这些3D锚点，我们通过两种互补方式改进2D表示学习。几何感知的语义细化模块在3D引导下缓解2D特征中可能冗余和视角偏倚的依赖。此外，统一视角关系蒸馏模块将3D结构关系转移到2D特征，提升跨视角对齐同时保持2D-only推理流程。在多个基准测试中的广泛实验表明，GeoLink在多个基准测试中一致优于现有方法，并在未见领域和多变天气中实现更优的泛化能力。
英文摘要：
Generalizable cross-view geo-localization aims to match the same location across views in unseen regions and conditions without GPS supervision. Its core difficulty lies in severe semantic inconsistency caused by viewpoint variation and poor generalization under domain shift. Existing methods mainly rely on 2D correspondence, but they are easily distracted by redundant shared information across views, leading to less transferable representations. To address this, we propose GeoLink, a 3D-aware semantic-consistent framework for Generalizable cross-view geo-localization. Specifically, we offline reconstruct scene point clouds from multi-view drone images using VGGT, providing stable structural priors. Based on these 3D anchors, we improve 2D representation learning in two complementary ways. A Geometric-aware Semantic Refinement module mitigates potentially redundant and view-biased dependencies in 2D features under 3D guidance. In addition, a Unified View Relation Distillation module transfers 3D structural relations to 2D features, improving cross-view alignment while preserving a 2D-only inference pipeline. Extensive experiments on multiple benchmarks show that GeoLink consistently outperforms state-of-the-art methods and achieves superior generalization across unseen domains and diverse weather environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
