AirZoo: A Unified Large-Scale Dataset for Grounding Aerial Geometric 3D Vision

Despite the rapid progress in data-driven 3D vision, aerialgeometric 3D vision remains a formidable challenge due to the severescarcity of large-scale, high-fidelity training data. Existing benchmarks,predominantly biased toward ground-level or object-centric views, donot account for complex viewpoint transformations and diverse envi-ronmental conditions in UAV-based sensing. To bridge this critical gap,we propose AirZoo, a unified large-scale dataset and benchmark forgrounding aerial geometric 3D vision. AirZoo possesses three appeal-ing properties: 1) Scalable Generation Pipeline: Leveraging freelyavailable, world-scale photogrammetric 3D meshes, it renders vast out-door environments with customizable UAV flight trajectories and config-urable weather/illumination. 2) Comprehensive Scene Diversity: Itprovides extensive global coverage (22 countries, 95 base flight sequences,and 377 weather-conditioned trajectories), systematically encompassingboth highly structured urban landscapes and complex unstructured nat-ural environments. 3) Rich Geometric Annotations: Each frameprovides synchronized, pixel-level metric depth and precise 6-DoF geo-referenced poses, essential for geometry-aware learning. Through threerigorous evaluation tracks—aerial image retrieval, cross-view matching,and multi-view 3D reconstruction—we demonstrate that AirZoo servesas a powerful "pre-training engine." Extensive experiments on both pub-lic and newly collected real-world benchmarks reveal that fine-tuning onAirZoo yields substantial performance gains for state-of-the-art models(e.g., MegaLoc, RoMa, VGGT, and Depth Anything 3), establishing anew performance upper bound for aerial spatial intelligence.