用统一辐射场融合视觉与无线数据，构建高精度3D无线地图
Bridging Visual and Wireless Sensing via a Unified Radiation Field for 3D Radio Map Construction
- 基于3D高斯溅射与逆渲染，统一建模视觉与无线信号传播
- 空间谱精度提升24.7%，采样效率提高10倍，无需重新训练
- 适用于基站部署与机器人路径规划，适合智能环境感知系统
下一代无线网络亟需高保真环境感知能力。3D无线地图将物理环境与电磁波传播相连接，用于频谱规划与环境感知。然而，现有方法通常将视觉与无线数据视为独立模态，未能利用共享的电磁波传播规律。为此，我们提出URF-GS：一种基于3D高斯溅射与逆渲染的统一无线电-光学辐射场框架，用于3D无线地图构建。通过融合跨模态观测，该方法可恢复场景几何与材质属性，实现任意收发器配置下的无线信号预测，且无需重新训练。实验表明，相比基于NeRF的方法，空间谱精度最高提升24.7%，采样效率提高10倍。我们进一步在Wi-Fi接入点部署与机器人路径规划任务中验证了其有效性。该统一视觉-无线表征支持未来无线通信系统的全息辐射场建模。
原文摘要 · Abstract (English)
The emerging applications of next-generation wireless networks demand high-fidelity environmental intelligence. 3D radio maps bridge physical environments and electromagnetic propagation for spectrum planning and environment-aware sensing. However, most existing methods treat visual and wireless data as independent modalities and fail to leverage shared electromagnetic propagation principles. To bridge this gap, we propose URF-GS, a unified radio-optical radiation field framework based on 3D Gaussian splatting and inverse rendering for 3D radio map construction. By fusing cross-modal observations, our method recovers scene geometry and material properties to predict radio signals under arbitrary transceiver configurations without retraining. Experiments demonstrate up to a 24.7% improvement in spatial spectrum accuracy and a 10x increase in sample efficiency compared with NeRF-based methods. We further showcase URF-GS in Wi-Fi AP deployment and robot path planning tasks. This unified visual-wireless representation supports holistic radiation field modeling for future wireless communication systems.
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。