用游戏引擎生成同步多视角可见光与热成像数据，解决无人机目标检测数据难题。
G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection
- 基于Arma3游戏引擎生成同步多视角RGB-T数据，可精确控制拍摄角度。
- 自动生成边界框，实现高精度标注，节省人工成本。
- 适用于研究视角变化、多模态融合和仿真到真实迁移的场景。
本文提出G-MAD，一个基于Arma3游戏引擎的开源框架，用于生成同步多视角可见光-热成像（RGB-T）数据，支持无人机目标检测任务。该框架克服了真实空中数据集构建中的关键挑战：视角控制不足、多模态对齐不完善以及标注成本高。G-MAD支持结构化场景定义、可控的多视角相机布局、可见光与热成像同步采集，并利用引擎级几何元数据实现自动边界框标注。这些能力使研究者可开展视角变化影响、多模态融合效果及合成到真实迁移等受控实验。此外，基于G-MAD我们构建并发布了AMOD——一个大规模多视角空中RGB-T目标检测基准数据集。项目源码与数据集已公开于https://unique-chan.github.io/G-MAD-Project。
原文摘要 · Abstract (English)
This work introduces G-MAD, an open-source framework that uses Arma3 to generate synchronized multi-view RGB-T data for aerial object detection. G-MAD addresses key limitations of real-world aerial dataset construction, including limited viewpoint control, imperfect RGB-T alignment and high annotation cost. The framework supports structured scenario specification, controllable multi-view camera placement, simultaneous visible/thermal capture, and automatic bounding box annotation using engine-level geometric metadata. These capabilities enable controlled studies of viewpoint variation, multi-modal fusion, and synthetic-to-real transfer in aerial object detection. Besides, using G-MAD, we construct and release AMOD, a new large-scale multi-view aerial RGB-T object detection benchmark. The source code and the dataset are available at https://unique-chan.github.io/G-MAD-Project.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。