Precisely modeling radio propagation in complex environments has been a significant challenge, especially with the advent of 5G and beyond networks, where managing massive antenna arrays demands more detailed information. Traditional methods, such as empirical models and ray tracing, often fall short, either due to insufficient details or because of challenges for real-time applications. Inspired by the newly proposed 3D Gaussian Splatting method in the computer vision domain, which outperforms other methods in reconstructing optical radiance fields, we propose RF-3DGS, a novel approach that enables precise site-specific reconstruction of radio radiance fields from sparse samples. RF-3DGS can render radio spatial spectra at arbitrary positions within 2 ms following a brief 3-minute training period, effectively identifying dominant propagation paths. Furthermore, RF-3DGS can provide fine-grained Spatial Channel State Information (Spatial-CSI) of these paths, including the channel gain, the delay, the angle of arrival (AoA), and the angle of departure (AoD). Our experiments, calibrated through real-world measurements, demonstrate that RF-3DGS not only significantly improves reconstruction quality, training efficiency, and rendering speed compared to state-of-the-art methods, but also holds great potential for supporting wireless communication and advanced applications such as Integrated Sensing and Communication (ISAC). Code and dataset will be available at https://github.com/SunLab-UGA/RF-3DGS.

翻译：在复杂环境中精确建模无线电传播一直是一个重大挑战，特别是在5G及未来网络时代，管理大规模天线阵列需要更详细的信息。传统方法，如经验模型和射线追踪，往往存在不足，要么因为细节不够充分，要么难以满足实时应用的需求。受计算机视觉领域新提出的、在重建光学辐射场方面优于其他方法的3D高斯溅射方法的启发，我们提出了RF-3DGS，这是一种新颖的方法，能够从稀疏样本中精确地重建特定场景的无线辐射场。RF-3DGS在短暂的3分钟训练后，能够在2毫秒内渲染任意位置的无线空间谱，有效识别主导传播路径。此外，RF-3DGS能够提供这些路径的细粒度空间信道状态信息，包括信道增益、时延、到达角和离开角。我们通过实际测量校准的实验表明，与现有最先进的方法相比，RF-3DGS不仅显著提高了重建质量、训练效率和渲染速度，而且在支持无线通信及高级应用（如集成感知与通信）方面具有巨大潜力。代码和数据集将在 https://github.com/SunLab-UGA/RF-3DGS 提供。