构建首个像素级无人机与卫星图像地理对齐数据集，实现精准定位。
Pixel-wise Geo-registration of Drone and Satellite Images
- 提出基于密集像素的跨视角地理配准方法
- 在多种场景和相机配置下实现高精度对齐
- 适合遥感、自动驾驶等需要精确地理定位的领域
像素级跨视图地理配准旨在将查询图像（如无人机图像）与地理参考的卫星地图对齐，使每个查询像素都能映射到真实世界经纬度。尽管跨视图地理定位取得显著进展，现有基准大多仅提供单个坐标标签，限制了评估范围，导致密集地理对齐研究不足。为此，我们提出 SkyReg，一个用于像素级无人机到卫星图像地理配准的数据集与标准化基准，涵盖正射与透视等多种视角、城市、地标中心、郊区/农村等多样化场景以及不同相机配置，提供每像素的地理标注监督。利用 SkyReg，我们评估了涵盖检索、特征匹配、单应性对齐及前馈3D重建在内的广泛基线方法。最终，基于 SkyReg 的跨视图图像对训练出的几何感知重建流程达到当前最优性能，显著提升精度。
原文摘要 · Abstract (English)
Pixel-level cross-view geo-registration aims to align a query image (e.g., drone) to a geo-referenced satellite map so that every query pixel can be mapped to real-world GPS coordinates. Despite strong progress in cross-view geo-localization, existing benchmarks largely provide only GPS labels, limiting evaluation to a single coordinate per image and leaving dense geodetic alignment underexplored. We introduce SkyReg, a dataset and standardized benchmark for pixel-level drone-to-satellite geo-registration, providing dense per-pixel geo-location supervision across diverse settings (orthographic and perspective), scene types (urban, landmark-centric, suburban/rural), and camera configurations. Using SkyReg, we evaluate a broad set of baselines spanning retrieval, feature matching, homography-based alignment, and feed-forward 3D reconstruction. Finally, cross-view pairs from SkyReg, we train a geometry-aware reconstruction pipeline that achieves state-of-the-art results,improving performance by a significant margin.
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