AeroScene: Progressive Scene Synthesis for Aerial Robotics
This work addresses the time-consuming and difficult-to-scale manual scene creation in drone simulators, offering a solution for aerial robotics applications like navigation and landing.
The paper tackles the problem of generating realistic 3D scenes for drone simulators, which currently rely on manual efforts, by introducing AeroScene, a hierarchical diffusion model that outperforms prior methods and generates over 1,000 physics-ready scenes for aerial robotics tasks.
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 AeroScene significantly outperforms prior methods. Furthermore, we use AeroScene to generate a large-scale dataset of over 1,000 physics-ready, high fidelity 3D scenes that can be directly integrated into NVIDIA Isaac Sim. Finally, we illustrate the utility of these generated environments on downstream drone navigation tasks. Our code and dataset are publicly available at aioz-ai.github.io/AeroScene/