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

Abstract

Despite rapid progress in data-driven 3D vision, aerial geometric 3D vision remains limited by the scarcity of large-scale, high-fidelity training data. AirZoo bridges this gap with a unified dataset and benchmark for UAV-based sensing. It combines a scalable generation pipeline built on world-scale photogrammetric 3D meshes, comprehensive scene diversity across 378 regions in 22 countries, and rich geometric annotations including pixel-aligned metric depth, camera intrinsics, and precise 6-DoF geo-referenced poses. Through aerial image retrieval, cross-view matching, and multi-view 3D reconstruction, AirZoo acts as a pre-training engine that improves state-of-the-art models such as MegaLoc, RoMa, VGGT, and Depth Anything 3 on real-world aerial benchmarks.

Dataset Construction

AirZoo uses Cesium for Unreal to stream Google 3D Tiles into UE5, then drives a custom AirSim-Cesium-Unreal simulator over global terrains. The pipeline logs continuous UAV video sequences rather than isolated screenshots, producing synchronized RGB images, dense metric depth, calibrated intrinsics, and Earth-fixed 6-DoF poses. Weather and illumination are systematically varied along the same trajectories, encouraging models to learn geometry that survives appearance changes.

Scale and Geometry

The dataset contains over 1.2 million high-resolution frames at 1600 × 1200 pixels, collected from nearly 2,386 km of simulated flight trajectories. Each region is rendered under multiple weather and time-of-day conditions, while the camera envelope spans 0-800 m altitude and 10°-90° pitch angles, covering oblique-to-nadir UAV viewpoints.

Bidirectional projection checks report a 0.066% median relative depth error, with P90 at 0.174% and P95 at 0.380%.

AirZoo-Real Benchmark

AirZoo also evaluates real-flight transfer through AirZoo-Real, a collected benchmark with RTK-aligned UAV imagery. The reconstruction split includes 9,430 images from four areas captured across 06:00-08:00, 12:00-14:00, and 18:00-20:00, with extra 22:00-24:00 low-light flights for two scenes. The benchmark resources are available through the Evaluation Benchmarks link above.

Evaluation Tracks

1Aerial Image Retrieval

Given a UAV query, the retrieval task finds the most relevant geo-tagged satellite tile under large viewpoint, scale, illumination, and seasonal changes. AirZoo fine-tuning improves top-rank retrieval quality, especially on the harder AirZoo-Real split with arbitrary UAV viewpoints.

2Cross-view Matching

Matching orthophoto references to oblique UAV imagery is central to UAV pose estimation. AirZoo exposes RoMa to difficult orthophoto-to-oblique pairs with controlled overlap, improving correspondence quality under altitude, viewpoint, and weather variation.

3Multi-view 3D Reconstruction

UAV sequences introduce wide baselines, strong oblique views, and lighting changes that ground-view training data rarely covers. Fine-tuning VGGT and DA3 on AirZoo consistently improves reconstruction performance across synthetic and real aerial evaluations.