用点云建模无线电传播，一次训练跨场景通用预测。
Point2Radio: A Foundation Model for Cross-Scene Radio Fields from Material-Aware Point Clouds
- 从多场景材料点云学习可迁移的传播先验。
- 3D路径增益预测均方误差仅0.871 dB，比基线低76.7%。
- 毫秒级推理，无需网格或路径追踪，适合实时应用。
高保真无线电场通常需为每个场景单独模拟或拟合，难以利用跨环境共享的传播结构。本文提出Point2Radio，一种从多个环境学习可迁移传播先验的基础模型。给定材料感知点云和发射机（TX）配置，统一编码器生成条件化的场景表征，可在任意接收机（RX）位置查询。特定任务解码器将该表征映射至不同无线电量，如三维（3D）路径增益（PG）场和功率角谱（PAS）。在包含86,272个TX条件场的337场景数据集的场景互斥划分上评估，模型实现0.871 dB的平均绝对误差（MAE），相比同分割的UNet基线降低76.7%。同一编码器通过任务专用解码器支持PAS预测。实验还表明，轻量级目标场景微调可进一步提升对特定环境的适应能力。
原文摘要 · Abstract (English)
High-fidelity radio fields are typically simulated for every scene--transmitter configuration or fitted separately to each scene, failing to exploit propagation structures shared across environments. We present Point2Radio, a foundation model that learns a transferable propagation prior from multiple environments. Given a material-aware point cloud and a transmitter (TX) setting, a common encoder produces a TX-conditioned scene representation that can be queried at arbitrary receiver (RX) locations. Task-specific query decoders map this representation to different radio quantities, e.g., three-dimensional (3D) path-gain (PG) fields and power angular spectra (PAS). At inference for a new scene, the model uses only a material-aware point cloud and transceiver queries, running in milliseconds on a single GPU without meshes or explicit path tracing. We evaluate PG prediction on a scene-disjoint split of a 337-scene corpus containing 86,272 TX-conditioned fields. Point2Radio achieves 0.871 dB mean absolute error (MAE), reducing error by 76.7% relative to a same-split UNet-style baseline. The same encoder also supports PAS prediction via a task-specific decoder. Experiments further show that light target-scene fine-tuning improves adaptation to a specific environment.
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