通过无人机构建3D场景，实现街景与卫星图的精准定位匹配。
SkyLink: Unifying Street-Satellite Geo-Localization via UAV-Mediated 3D Scene Alignment
- 用无人机多尺度影像构建3D场景，作为街景与卫星图的桥梁。
- 在UAVM2025挑战赛中，大学1652数据集上达到25.75% Recall@1。
- 适合做跨视角地理定位、城市智能导航的研究者使用。
跨视图地理定位旨在建立不同视角间的地理位置对应关系。现有方法通常通过直接特征相似性匹配学习跨视图关联，常忽略极端视角差异带来的语义退化问题。为此，本文聚焦于视角变化下的鲁棒特征检索，提出SkyLink新方法。首先利用Google检索增强模块对街景图像进行数据增强，缓解因街景视角受限导致的关键目标遮挡问题。进一步采用局部感知特征聚合模块，强化多区域特征聚合，确保跨视角特征提取的一致性。同时，利用多尺度无人机影像构建的3D场景信息作为街景与卫星视角之间的桥梁，通过自监督与跨视图对比学习实现特征对齐。实验结果表明，该方法在多种城市场景下具备强鲁棒性与泛化能力，在UAVM2025挑战赛的University-1652数据集上取得25.75% Recall@1的准确率。代码将发布于https://github.com/HRT00/CVGL-3D。
原文摘要 · Abstract (English)
Cross-view geo-localization aims at establishing location correspondences between different viewpoints. Existing approaches typically learn cross-view correlations through direct feature similarity matching, often overlooking semantic degradation caused by extreme viewpoint disparities. To address this unique problem, we focus on robust feature retrieval under viewpoint variation and propose the novel SkyLink method. We firstly utilize the Google Retrieval Enhancement Module to perform data enhancement on street images, which mitigates the occlusion of the key target due to restricted street viewpoints. The Patch-Aware Feature Aggregation module is further adopted to emphasize multiple local feature aggregations to ensure the consistent feature extraction across viewpoints. Meanwhile, we integrate the 3D scene information constructed from multi-scale UAV images as a bridge between street and satellite viewpoints, and perform feature alignment through self-supervised and cross-view contrastive learning. Experimental results demonstrate robustness and generalization across diverse urban scenarios, which achieve 25.75$\%$ Recall@1 accuracy on University-1652 in the UAVM2025 Challenge. Code will be released at https://github.com/HRT00/CVGL-3D.
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