We present GRaF, Generalizable Radio-Frequency (RF) Radiance Fields, a framework that models RF signal propagation to synthesize spatial spectra at arbitrary transmitter or receiver locations, where each spectrum measures signal power across all surrounding directions at the receiver. Unlike state-of-the-art methods that adapt vanilla Neural Radiance Fields (NeRF) to the RF domain with scene-specific training, GRaF generalizes across scenes to synthesize spectra. To enable this, we prove an interpolation theory in the RF domain: the spatial spectrum from a transmitter can be approximated using spectra from geographically proximate transmitters. Building on this theory, GRaF comprises two components: (i) a geometry-aware Transformer encoder that captures spatial correlations from neighboring transmitters to learn a scene-independent latent RF radiance field, and (ii) a neural ray tracing algorithm that estimates spectrum reception at the receiver. Experimental results demonstrate that GRaF outperforms existing methods on single-scene benchmarks and achieves state-of-the-art performance on unseen scene layouts.

翻译：我们提出GRaF（可泛化射频辐射场），这是一个模拟射频信号传播的框架，可在任意发射器或接收器位置合成空间频谱，其中每个频谱测量接收器周围所有方向的信号功率。与采用场景特定训练将基础神经辐射场（NeRF）适配到射频领域的最新方法不同，GRaF能够跨场景泛化以合成频谱。为实现这一目标，我们证明了射频领域中的插值理论：来自发射器的空间频谱可通过地理邻近发射器的频谱进行近似。基于该理论，GRaF包含两个组件：(i) 几何感知Transformer编码器，用于从相邻发射器捕获空间相关性以学习与场景无关的潜在射频辐射场；(ii) 神经光线追踪算法，用于估计接收器处的频谱接收。实验结果表明，GRaF在单场景基准测试上优于现有方法，并在未见过的场景布局中达到了最先进性能。