AirSim360：一个无人机视角下的全景仿真平台
AirSim360: A Panoramic Simulation Platform within Drone View
为全景/无人机视角下的深度估计、语义与实例分割、导航等任务提供带像素级几何与语义标注的合成数据源，并给出可自动生成飞行轨迹的仿真接口，可直接用于训练与评测 360 度空间理解模型。
原文依据 we propose AirSim360, a simulation platform for omnidirectional data from aerial viewpoints
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论360 度全向理解领域在推动空间智能方面正受到越来越多的关注。然而，大规模且多样化的数据匮乏仍是主要限制。在本工作中，我们提出 AirSim360，一个面向空中视角全向数据的仿真平台，可借助无人机进行大范围场景采样。具体而言，AirSim360 聚焦三个关键方面：面向像素级几何、语义与实例级理解的渲染对齐数据与标注范式；用于建模人类行为的交互式行人感知系统；以及支持导航任务的自动化轨迹生成范式。此外，我们采集了超过 6 万个全景样本，并在多种任务上开展了大量实验，以验证该仿真器的有效性。与现有仿真器不同，我们的工作是首个在全向设定下对 4D 真实世界进行系统化建模的工作。整个平台，包括工具包、插件以及所采集的数据集，都将公开提供。
英文原文摘要 1076 字符
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 instance-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.