Radio frequency (RF) propagation modeling poses unique electromagnetic simulation challenges. While recent neural representations have shown success in visible spectrum rendering, the fundamentally different scales and physics of RF signals require novel modeling paradigms. In this paper, we introduce RFScape, a novel framework that bridges the gap between neural scene representation and RF propagation modeling. Our key insight is that complex RF-object interactions can be captured through object-centric neural representations while preserving the composability of traditional ray tracing. Unlike previous approaches that either rely on crude geometric approximations or require dense spatial sampling of entire scenes, RFScape learns per-object electromagnetic properties and enables flexible scene composition. Through extensive evaluation on real-world RF testbeds, we demonstrate that our approach achieves 13 dB improvement over conventional ray tracing and 5 dB over state-of-the-art neural baselines in modeling accuracy while requiring only sparse training samples.

翻译：射频传播建模带来了独特的电磁仿真挑战。尽管近期神经表示在可见光谱渲染中取得了成功，但射频信号在尺度与物理机制上的根本差异要求新的建模范式。本文提出RFScape——一个连接神经场景表示与射频传播建模的创新框架。我们的核心洞见在于：通过以对象为中心的神经表示可以捕捉复杂的射频-物体相互作用，同时保持传统射线追踪的可组合性。与以往依赖粗糙几何近似或需要对整个场景进行密集空间采样的方法不同，RFScape能够学习每个物体的电磁特性并实现灵活的场景组合。通过对真实世界射频测试平台的广泛评估，我们证明该方法在建模精度上比传统射线追踪提升13 dB，比最先进的神经基线提升5 dB，且仅需稀疏的训练样本。