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.

翻译：下一代无线网络的新兴应用亟需高保真环境智能感知。三维无线电地图连接物理环境与电磁传播，服务于频谱规划与环境感知。然而，现有方法大多将视觉与无线数据视为独立模态，未能利用共享的电磁传播原理。为弥合这一鸿沟，我们提出URF-GS——一种基于三维高斯泼溅与逆渲染的统一光-电辐射场框架，用于构建三维无线电地图。该方法通过融合跨模态观测值，恢复场景几何与材料属性，无需重新训练即可预测任意收发配置下的无线信号。实验表明，相比于基于NeRF的方法，我们的方法在空间频谱精度上提升高达24.7%，样本效率提升10倍。我们进一步将URF-GS应用于Wi-Fi接入点部署与机器人路径规划任务。这种统一的视觉-无线表示支持未来无线通信系统的全息辐射场建模。