The field of 360-degree omnidirectional understanding has been receiving increasing attention for advancing spatial intelligence. However, the lack of large-scale and diverse data remains a major limitation. In this work, we propose AirSim360, a simulation platform for omnidirectional data from aerial viewpoints, enabling wide-ranging scene sampling with drones. Specifically, AirSim360 focuses on three key aspects: a render-aligned data and labeling paradigm for pixel-level geometric, semantic, and entity-level understanding; an interactive pedestrian-aware system for modeling human behavior; and an automated trajectory generation paradigm to support navigation tasks. Furthermore, we collect more than 60K panoramic samples and conduct extensive experiments across various tasks to demonstrate the effectiveness of our simulator. Unlike existing simulators, our work is the first to systematically model the 4D real world under an omnidirectional setting. The entire platform, including the toolkit, plugins, and collected datasets, will be made publicly available at https://insta360-research-team.github.io/AirSim360-website.
@article{arxiv.2512.02009,
title = {AirSim360: A Panoramic Simulation Platform within Drone View},
author = {Xian Ge and Yuling Pan and Yuhang Zhang and Xiang Li and Weijun Zhang and Dizhe Zhang and Zhaoliang Wan and Xin Lin and Xiangkai Zhang and Juntao Liang and Jason Li and Wenjie Jiang and Bo Du and Ming-Hsuan Yang and Lu Qi},
journal= {arXiv preprint arXiv:2512.02009},
year = {2025}
}