AirSim360: A Panoramic Simulation Platform within Drone View

2025-12-01

AI Summary

Current 360° omnidirectional understanding research is hindered by the scarcity of large-scale, diverse real-world aerial-view data. To address this, we propose the first drone-centric panoramic simulation platform, enabling systematic 4D dynamic scene modeling in panoramic settings for the first time. Our method introduces three key innovations: (1) a rendering-aligned pixel-level joint geometric-semantic annotation paradigm; (2) pedestrian-aware interaction modeling; and (3) behavior-constrained automatic trajectory synthesis. Integrating panoramic rendering, entity-level semantic generation, and navigation-policy-driven path planning, we construct a multi-scene, multi-task dataset comprising over 60,000 samples. Extensive experiments demonstrate significant improvements on downstream tasks—including panoramic navigation and spatial semantic understanding—validating the platform’s effectiveness. This work establishes a scalable, high-fidelity benchmark for drone vision and spatial intelligence research.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data generation and simulation

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.

Problem

• Addresses lack of large-scale omnidirectional aerial data
• Models human behavior in panoramic drone simulations
• Supports navigation with automated trajectory generation

Innovation

• Simulation platform for aerial omnidirectional data
• Render-aligned labeling for pixel-level scene understanding
• Automated trajectory generation for drone navigation tasks