Cross-view geo-localization (CVGL) is pivotal for GNSS-denied UAV navigation but remains brittle under the drastic geometric misalignment between oblique aerial views and orthographic satellite references. Existing methods predominantly operate within a 2D manifold, neglecting the underlying 3D geometry where view-dependent vertical facades (macro-structure) and scale variations (micro-scale) severely corrupt feature alignment. To bridge this gap, we propose (MGS)$^2$, a geometry-grounded framework. The core of our innovation is the Macro-Geometric Structure Filtering (MGSF) module. Unlike pixel-wise matching sensitive to noise, MGSF leverages dilated geometric gradients to physically filter out high-frequency facade artifacts while enhancing the view-invariant horizontal plane, directly addressing the domain shift. To guarantee robust input for this structural filtering, we explicitly incorporate a Micro-Geometric Scale Adaptation (MGSA) module. MGSA utilizes depth priors to dynamically rectify scale discrepancies via multi-branch feature fusion. Furthermore, a Geometric-Appearance Contrastive Distillation (GACD) loss is designed to strictly discriminate against oblique occlusions. Extensive experiments demonstrate that (MGS)$^2$ achieves state-of-the-art performance, recording a Recall@1 of 97.5\% on University-1652 and 97.02\% on SUES-200. Furthermore, the framework exhibits superior cross-dataset generalization against geometric ambiguity. The code is available at: \href{https://github.com/GabrielLi1473/MGS-Net}{https://github.com/GabrielLi1473/MGS-Net}.

翻译：跨视角地理定位对于GNSS拒止环境下的无人机导航至关重要，但在倾斜航拍视图与正射卫星参考图像之间存在剧烈几何错位时，其性能仍然脆弱。现有方法主要在二维流形上操作，忽略了底层三维几何结构，其中视角依赖的垂直立面（宏观结构）和尺度变化（微观尺度）严重破坏了特征对齐。为弥补这一差距，我们提出了(MGS)$^2$，一个基于几何的框架。我们创新的核心是宏观几何结构滤波模块。与对噪声敏感的逐像素匹配不同，MGSF利用膨胀的几何梯度从物理上滤除高频立面伪影，同时增强视角不变的水平平面，直接应对域偏移。为确保此结构滤波具有鲁棒的输入，我们显式地引入了微观几何尺度自适应模块。MGSA利用深度先验，通过多分支特征融合动态校正尺度差异。此外，我们设计了几何-外观对比蒸馏损失，以严格区分倾斜视角下的遮挡。大量实验表明，(MGS)$^2$实现了最先进的性能，在University-1652数据集上Recall@1达到97.5\%，在SUES-200数据集上达到97.02\%。此外，该框架在应对几何模糊性方面展现出卓越的跨数据集泛化能力。代码发布于：\href{https://github.com/GabrielLi1473/MGS-Net}{https://github.com/GabrielLi1473/MGS-Net}。