The emerging applications of next-generation wireless networks (e.g., immersive 3D communication, low-altitude networks, and integrated sensing and communication) necessitate high-fidelity environmental intelligence. 3D radio maps have emerged as a critical tool for this purpose, enabling spectrum-aware planning and environment-aware sensing by bridging the gap between physical environments and electromagnetic signal propagation. However, constructing accurate 3D radio maps requires fine-grained 3D geometric information and a profound understanding of electromagnetic wave propagation. Existing approaches typically treat optical and wireless knowledge as distinct modalities, failing to exploit the fundamental physical principles governing both light and electromagnetic propagation. To bridge this gap, we propose URF-GS, a unified radio-optical radiation field representation framework for accurate and generalizable 3D radio map construction based on 3D Gaussian splatting (3D-GS) and inverse rendering. By fusing visual and wireless sensing observations, URF-GS recovers scene geometry and material properties while accurately predicting radio signal behavior at arbitrary transmitter-receiver (Tx-Rx) configurations. Experimental results demonstrate that URF-GS achieves up to a 24.7% improvement in spatial spectrum prediction accuracy and a 10x increase in sample efficiency for 3D radio map construction compared with neural radiance field (NeRF)-based methods. This work establishes a foundation for next-generation wireless networks by integrating perception, interaction, and communication through holistic radiation field reconstruction.

翻译：下一代无线网络的新兴应用（如沉浸式三维通信、低空网络及通感一体化）对高保真环境智能提出了迫切需求。三维无线电地图已成为实现这一目标的关键工具，通过弥合物理环境与电磁信号传播之间的鸿沟，支持频谱感知规划与环境感知传感。然而，构建精确的三维无线电地图需要细粒度的三维几何信息以及对电磁波传播的深刻理解。现有方法通常将光学与无线知识视为独立模态，未能充分利用光与电磁传播所遵循的基本物理原理。为弥合这一差距，我们提出URF-GS——一种基于三维高斯溅射（3D-GS）与逆向渲染的统一无线电-光学辐射场表征框架，用于实现精确且可泛化的三维无线电地图构建。通过融合视觉与无线感知观测数据，URF-GS在恢复场景几何与材质属性的同时，能精准预测任意发射-接收（Tx-Rx）配置下的无线电信号行为。实验结果表明，相较于基于神经辐射场（NeRF）的方法，URF-GS在空间频谱预测精度上最高提升24.7%，并在三维无线电地图构建的样本效率上实现10倍提升。本研究通过整体辐射场重构整合感知、交互与通信，为下一代无线网络奠定了理论基础。