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

arXiv: https://arxiv.org/abs/2607.19942 · HTML: https://mlsanity.com/paper/2607.19942/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 78 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 26 of 26)

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- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark](https://mlsanity.com/paper/1909.00133/) (2019)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges](https://mlsanity.com/paper/2102.12219/) (2021)
- [Drone-based Object Counting by Spatially Regularized Regional Proposal Network](https://mlsanity.com/paper/1707.05972/) (2017)
- [Drone-based RGB-Infrared Cross-Modality Vehicle Detection via Uncertainty-Aware Learning](https://mlsanity.com/paper/2003.02437/) (2020)
- [Learning from Synthetic Data for Crowd Counting in the Wild](https://mlsanity.com/paper/1903.03303/) (2019)
- [xView: Objects in Context in Overhead Imagery](https://mlsanity.com/paper/1802.07856/) (2018)
- [FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery](https://mlsanity.com/paper/2103.05569/) (2021)
- [MMRotate: A Rotated Object Detection Benchmark using PyTorch](https://mlsanity.com/paper/2204.13317/) (2022)
- [Anchor-free Oriented Proposal Generator for Object Detection](https://mlsanity.com/paper/2110.01931/) (2021)
- [Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging Labels](https://mlsanity.com/paper/2301.12689/) (2023)
- [SpaceNet MVOI: a Multi-View Overhead Imagery Dataset](https://mlsanity.com/paper/1903.12239/) (2019)
- [Deformation and Correspondence Aware Unsupervised Synthetic-to-Real Scene Flow Estimation for Point Clouds](https://mlsanity.com/paper/2203.16895/) (2022)
- [Game4Loc: A UAV Geo-Localization Benchmark from Game Data](https://mlsanity.com/paper/2409.16925/) (2024)
- [Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains](https://mlsanity.com/paper/2404.15882/) (2024)
- [AMFD: Distillation via Adaptive Multimodal Fusion for Multispectral Pedestrian Detection](https://mlsanity.com/paper/2405.12944/) (2024)
- [Edify 3D: Scalable High-Quality 3D Asset Generation](https://mlsanity.com/paper/2411.07135/) (2024)
- [Imbalanced Image Classification with Complement Cross Entropy](https://mlsanity.com/paper/2009.02189/) (2020)
- [When Schr"odinger Bridge Meets Real-World Image Dehazing with Unpaired Training](https://mlsanity.com/paper/2507.09524/) (2025)
- [GenAssets: Generating in-the-wild 3D Assets in Latent Space](https://mlsanity.com/paper/2604.23010/) (2026)
- [Adaptive Image Zoom-in with Bounding Box Transformation for UAV Object Detection](https://mlsanity.com/paper/2602.07512/) (2026)

## Abstract (arXiv)

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.