AirSim360: A Panoramic Simulation Platform within Drone View

TL;DR

AirSim360 is a novel panoramic simulation platform for aerial omnidirectional data, enabling scene understanding, human behavior modeling, and navigation, with extensive datasets and experiments demonstrating its effectiveness.

Contribution

It introduces the first systematic modeling of 4D real-world omnidirectional data from aerial viewpoints, including a comprehensive platform and datasets.

Findings

Collected over 60K panoramic samples.

Demonstrated effectiveness across various perception and navigation tasks.

First to model 4D real-world omnidirectional data systematically.

Abstract

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.