无人机与卫星图像的像素级地理配准
Pixel-wise Geo-registration of Drone and Satellite Images
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中文总结 AI 辅助
针对现有跨视角地理定位基准仅提供单坐标GPS标签、未充分探索密集配准的问题，推出SkyReg数据集与基准，评估多类基线并训练几何感知重建管线，实现无人机与卫星图像像素级地理配准的最优性能。
中文摘要 AI 辅助
像素级跨视角地理配准旨在将查询图像（如无人机图像）对齐至已地理参考的卫星地图，使每个查询像素都能映射到现实世界的GPS坐标。尽管跨视角地理定位已取得显著进展，但现有基准大多仅提供GPS标签，将评估限制为每张图像单个坐标，导致密集大地测量对齐研究不足。我们推出SkyReg——一个用于无人机到卫星像素级地理配准的数据集与标准化基准，其在多样场景（正射与透视）、场景类型（城市、地标中心、郊区/乡村）及相机配置下提供逐像素密集地理定位监督。利用SkyReg，我们评估了涵盖检索、特征匹配、基于单应性的对齐及前馈3D重建的大量基线方法；最后，基于SkyReg的跨视角图像对，我们训练了几何感知重建管线，取得了最优结果，大幅提升了性能。
英文摘要
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.
发表机构
- Institute of Artificial Intelligence, University of Central Florida（中佛罗里达大学人工智能学院）
机构由 AI 辅助整理，请以论文原文为准。
