Geo2：几何引导的跨视角地理定位与图像合成
Geo2: Geometry-Guided Cross-view Geo-Localization and Image Synthesis
利用几何基础模型（如 VGGT）提取的三维几何先验，构建共享的三维感知潜空间来桥接地面与航拍视角，为跨视角定位与双向图像合成提供统一框架；其 GeoMap 和 GeoFlow 设计可复用于其他需要跨视角几何对齐的任务。
原文依据 we present Geo^2, a unified framework that leverages Geometric priors from GFMs (e.g., VGGT) to jointly perform geo-spatial tasks, CVGL and bidirectional CVIS.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论跨视角地理空间学习包含两个重要任务：跨视角地理定位（CVGL）和跨视角图像合成（CVIS），两者都依赖于建立地面与航拍视角之间的几何对应关系。近期，几何基础模型（GFMs）在从图像中提取可泛化的三维几何特征方面展现出强大能力，但其在跨视角地理空间任务中的潜力尚未被充分探索。本文提出 Geo^2，一个统一框架，利用 GFMs（如 VGGT）的几何先验来联合执行地理空间任务 CVGL 和双向 CVIS。尽管 GFMs 具备三维重建能力，但由于地面与航拍图像之间存在较大的视角差异，直接将其应用于 CVGL 和 CVIS 仍具挑战性。我们提出 GeoMap，将地面和航拍特征嵌入一个共享的三维感知潜空间，有效减少跨视角差异以利于定位。该共享潜空间自然地桥接了双向的跨视角图像合成。为利用这一点，我们提出 GeoFlow，一个以几何感知潜变量嵌入为条件的流匹配模型。我们进一步引入一致性损失，以强制两个合成方向之间的潜变量对齐，确保双向连贯性。在包括 CVUSA、CVACT 和 VIGOR 在内的标准基准上的大量实验表明，Geo^2 在定位和合成两方面均达到最优性能，突显了三维几何先验在跨视角地理空间学习中的有效性。
英文原文摘要 1564 字符
Cross-view geo-spatial learning consists of two important tasks: Cross-View Geo-Localization (CVGL) and Cross-View Image Synthesis (CVIS), both of which rely on establishing geometric correspondences between ground and aerial views. Recent Geometric Foundation Models (GFMs) have demonstrated strong capabilities in extracting generalizable 3D geometric features from images, but their potential in cross-view geo-spatial tasks remains underexplored. In this work, we present Geo^2, a unified framework that leverages Geometric priors from GFMs (e.g., VGGT) to jointly perform geo-spatial tasks, CVGL and bidirectional CVIS. Despite the 3D reconstruction ability of GFMs, directly applying them to CVGL and CVIS remains challenging due to the large viewpoint gap between ground and aerial imagery. We propose GeoMap, which embeds ground and aerial features into a shared 3D-aware latent space, effectively reducing cross-view discrepancies for localization. This shared latent space naturally bridges cross-view image synthesis in both directions. To exploit this, we propose GeoFlow, a flow-matching model conditioned on geometry-aware latent embeddings. We further introduce a consistency loss to enforce latent alignment between the two synthesis directions, ensuring bidirectional coherence. Extensive experiments on standard benchmarks, including CVUSA, CVACT, and VIGOR, demonstrate that Geo^2 achieves state-of-the-art performance in both localization and synthesis, highlighting the effectiveness of 3D geometric priors for cross-view geo-spatial learning.