| title | G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection |
|---|---|
| authors | Yechan Kim, JongHyun Park, Dongho Yoon, Namhoon Jung, Moongu Jeon |
| date | 2026-07-22 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.19942 |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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. |
cite
@misc{kim2026g,
title={G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection},
author={Yechan Kim and JongHyun Park and Dongho Yoon and Namhoon Jung and Moongu Jeon},
year={2026},
eprint={2607.19942},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.19942}
}