Building a site-specific propagation model typically requires either ray-tracing over detailed 3D maps or dense measurement campaigns. Both approaches are expensive and often infeasible for rapid deployments where geographic data is unavailable or outdated. We present PropSplat, a map-free propagation modeling method that reconstructs radio frequency (RF) fields using 3D anisotropic Gaussian primitives. Each Gaussian encodes a scalar path loss offset relative to an explicit baseline path loss model with a learnable path loss exponent. Gaussians are initialized along observed transmitter--receiver paths and optimized end-to-end to learn the propagation environment without external information like floor plans, terrain databases, or clutter data. We evaluate PropSplat against wireless radiance field methods NeRF$^2$, GSRF, and WRF-GS+ on two real-world datasets. On large-scale outdoor drive-tests spanning multiple topographical regions at six sub-6 GHz frequencies, PropSplat achieves 5.38 dB RMSE when training measurements are spaced 300m apart and outperforms WRF-GS+ (5.87 dB), GSRF (7.46 dB), and NeRF$^2$ (14.76 dB). On indoor Bluetooth Low Energy measurements, PropSplat achieves 0.19m mean localization error, an order of magnitude better than NeRF$^2$ (1.84m), while achieving near-identical received signal strength prediction accuracy. These results show that accurate site-specific propagation reconstruction is achievable from sparse RF-native measurements. The need for geographic data as a prerequisite for scalable RF environment modeling is reduced.

翻译：构建特定场地的传播模型通常需要依赖详细3D地图的射线追踪或密集的测量活动。这两种方法成本高昂，且在地理数据不可用或过时的情况下，对于快速部署往往不可行。我们提出PropSplat，一种无地图传播建模方法，通过3D各向异性高斯基元重建射频（RF）场。每个高斯基元编码相对于显式基线路径损耗模型（具有可学习路径损耗指数）的标量路径损耗偏移。高斯基元沿观测到的发射-接收路径初始化，并通过端到端优化学习传播环境，无需外部信息（如楼层平面图、地形数据库或杂波数据）。我们在两个真实世界数据集上，将PropSplat与无线辐射场方法NeRF²、GSRF和WRF-GS+进行对比评估。在覆盖多个地形区域的六次低于6 GHz频率的大规模户外路测中，当训练测量间距为300米时，PropSplat实现了5.38 dB的RMSE，优于WRF-GS+（5.87 dB）、GSRF（7.46 dB）和NeRF²（14.76 dB）。在室内蓝牙低功耗测量中，PropSplat实现了0.19米的平均定位误差，比NeRF²（1.84米）提升一个数量级，同时达到了几乎相同的接收信号强度预测精度。这些结果表明，从稀疏的RF原生测量中实现准确的特定场地传播重建是可行的，从而降低了地理数据作为可扩展RF环境建模前提的需求。