AeroScene: Progressive Scene Synthesis for Aerial Robotics
Nghia Vu, Tuong Do, Dzung Tran, Binh X. Nguyen, Hoan Nguyen, Erman Tjiputra, Quang D. Tran, Hai-Nguyen Nguyen, Anh Nguyen

TL;DR
AeroScene is a hierarchical diffusion model that synthesizes realistic 3D scenes for aerial robotics, improving simulation environments and aiding drone navigation tasks.
Contribution
Introduces AeroScene, a novel hierarchical diffusion model for progressive 3D scene synthesis tailored for aerial robotics applications.
Findings
Outperforms prior scene synthesis methods in experiments
Generates over 1,000 physics-ready 3D scenes for simulation
Enhances drone navigation by providing realistic synthetic environments
Abstract
Generative models have shown substantial impact across multiple domains, their potential for scene synthesis remains underexplored in robotics. This gap is more evident in drone simulators, where simulation environments still rely heavily on manual efforts, which are time-consuming to create and difficult to scale. In this work, we introduce AeroScene, a hierarchical diffusion model for progressive 3D scene synthesis. Our approach leverages hierarchy-aware tokenization and multi-branch feature extraction to reason across both global layouts and local details, ensuring physical plausibility and semantic consistency. This makes AeroScene particularly suited for generating realistic scenes for aerial robotics tasks such as navigation, landing, and perching. We demonstrate its effectiveness through extensive experiments on our newly collected dataset and a public benchmark, showing that…
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