[Paper] AirSim360: A Panoramic Simulation Platform within Drone View

Source: arXiv - 2512.02009v1

Overview

AirSim360 introduces a new simulation environment that lets researchers and developers generate massive amounts of 360° panoramic data from a drone’s perspective. By combining realistic rendering, pedestrian behavior modeling, and automated flight‑path creation, the platform tackles the chronic shortage of diverse omnidirectional datasets for computer‑vision tasks such as segmentation, depth estimation, and navigation.

Key Contributions

• Render‑aligned labeling pipeline – automatic pixel‑perfect ground‑truth for geometry, semantics, and object instances directly from the simulator.
• Pedestrian‑aware interaction module – realistic human motion models that react to the drone’s presence, enabling safe‑flight and human‑centric perception studies.
• Automated trajectory generator – a plug‑and‑play system that produces diverse flight paths for navigation, mapping, and inspection scenarios.
• Large‑scale dataset – >60 K high‑resolution 360° panoramas covering varied urban, suburban, and natural environments.
• Open‑source release – full toolkit, Unity/Unreal plugins, and dataset are publicly available, encouraging community extensions.

Methodology

AirSim360 builds on the popular AirSim drone simulator but extends it to omnidirectional rendering. The workflow consists of three stages:

1. Scene Construction & Rendering – 3D environments are populated with textured assets and a virtual 360° camera mounted on a drone. The renderer outputs equirectangular images while simultaneously extracting depth maps, surface normals, and semantic masks from the graphics engine.
2. Pedestrian Modeling – A behavior engine places agents on sidewalks, roads, and open spaces. Agents follow socially‑aware navigation policies (e.g., collision avoidance, group formation) and can be scripted to react to the drone’s proximity, providing realistic human‑drone interaction data.
3. Trajectory Synthesis – Using a combination of waypoint planners, physics‑based flight dynamics, and randomization (weather, lighting, wind), the system automatically generates thousands of flight trajectories that cover diverse viewpoints and motion patterns.

All components are exposed through Python APIs, allowing developers to script custom data‑generation pipelines without deep graphics knowledge.

Results & Findings

• Benchmarking on standard 360° tasks – Models trained on AirSim360 data achieved up to 12% higher IoU on panoramic semantic segmentation and 15% lower depth error compared with models trained on existing indoor‑only 360° datasets.
• Generalization – When fine‑tuned on a small real‑world drone dataset (≈2 K images), AirSim360‑pretrained networks converged 2× faster and reached 5% higher accuracy, demonstrating the simulator’s domain‑transfer capability.
• Human‑drone safety – Experiments with the pedestrian‑aware module showed that drones following the generated safe‑flight trajectories reduced simulated near‑miss incidents by 30% compared to naïve straight‑line paths.

These results confirm that the platform not only supplies abundant labeled data but also improves model robustness for real‑world aerial perception.

Practical Implications

• Rapid prototyping for autonomous drones – Engineers can generate task‑specific training data (e.g., inspection of power lines, search‑and‑rescue in forests) without costly field flights.
• Safety‑critical simulation – The pedestrian‑aware system enables testing of collision‑avoidance algorithms in crowded urban airspaces before real‑world deployment.
• Cross‑modal research – Because depth, semantics, and instance masks are synchronized with the panoramic image, developers can experiment with multimodal fusion (e.g., LiDAR‑camera alignment) in a controlled setting.
• Education & Hackathons – The open‑source toolkit lowers the barrier for students and startups to explore 360° perception, fostering innovation in spatial AI.

Limitations & Future Work

• Visual realism gap – While the renderer produces high‑quality graphics, subtle texture and lighting variations still differ from real aerial footage, which may affect fine‑grained texture learning.
• Pedestrian behavior scope – Current models cover basic walking and standing actions; more complex activities (e.g., cycling, vehicle interaction) are not yet simulated.
• Scalability to extreme weather – Simulated rain, fog, and wind are limited; future versions aim to incorporate physics‑based atmospheric effects for robustness testing.

The authors plan to expand the environment library, integrate more sophisticated human‑agent models, and explore domain‑adaptation techniques to bridge the remaining reality gap.

Authors

• Xian Ge
• Yuling Pan
• Yuhang Zhang
• Xiang Li
• Weijun Zhang
• Dizhe Zhang
• Zhaoliang Wan
• Xin Lin
• Xiangkai Zhang
• Juntao Liang
• Jason Li
• Wenjie Jiang
• Bo Du
• Ming-Hsuan Yang
• Lu Qi

Paper Information

• arXiv ID: 2512.02009v1
• Categories: cs.CV
• Published: December 1, 2025
• PDF: Download PDF