G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection

[2607.19942] G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2607.19942 (cs) [Submitted on 22 Jul 2026 ( v1 ), last revised 15 Aug 2026 (this version, v3)] 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 View a PDF of the paper titled G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection, by Yechan Kim and JongHyun Park and Dongho Yoon and Namhoon Jung and Moongu Jeon View PDF HTML (experimental) 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 this https URL . Comments: ACM Multimedia 2026 (Supplementary Material Included) Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2607.19942 [cs.CV] (or arXiv:2607.19942v3 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2607.19942 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yechan Kim Dr. [ view email ] [v1] Wed, 22 Jul 2026 09:15:01 UTC (3,699 KB) [v2] Mon, 27 Jul 2026 16:15:51 UTC (3,702 KB) [v3] Sat, 15 Aug 2026 07:37:28 UTC (3,679 KB) Full-text links: Access Paper: View a PDF of the paper titled G-MAD: A Game-Based Data Generation Framework for Multi-View RGB-T Aerial Object Detection, by Yechan Kim and JongHyun Park and Dongho Yoon and Namhoon Jung and Moongu Jeon View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2026-07 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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