用普通地理数据自动构建城市级无线信号模型，省去人工校准。
KODAMA: Multimodal Digital Twin Reconstruction for Urban RF Propagation Modelling
- 仅用航拍、激光雷达和街景图，自动生成可做射线追踪的数字孪生模型。
- 在3个城区测试中，未校准预测误差低于单个分贝，比全自动方法提升5.35 dB。
- 适合需要快速部署高精度无线信道模型的城市规划与通信设计人员。
传统三维重建追求几何精度或视觉真实感，而射频数字孪生（RFDT）的核心是能否真实复现通信信道行为。现有方法要么依赖粗糙自动化场景，要么需人工耗时数周逐站建模并校准。本文提出KODAMA，一个仅使用现成地理数据（航空影像、LiDAR、摄影测量）的全自动流程：从这些数据生成地形与封闭建筑网格，再通过加权多视角融合街景图还原立面细节、电磁材料属性和杂波信息，全程无需现场勘察或校准。在覆盖3.6至28 GHz的三个城区测试中，未校准的预测达到个位数均方根误差（RMSE），相比自动化基线点对点误差降低最高达5.35 dB，且与人工校准的手工模型差距仅0.22 dB。
原文摘要 · Abstract (English)
3D reconstruction typically strives for geometric fidelity or visual plausibility. Radio frequency digital twins (RFDT) are instead judged by whether communication channels behave in them as they do in the real world. RFDTs promise site-specific channel prediction but current practice forces a choice between coarse automated scenes and hand-built, measurement-calibrated models that take weeks to construct per-site. We present KODAMA, an automated pipeline that reconstructs ray tracing-ready RFDTs at city scale from off-the-shelf geospatial data alone: aerial imagery, LiDAR, and photogrammetry yield terrain and watertight building meshes, while exposure-weighted multi-view fusion of street-level imagery recovers fa\c{c}ade relief, electromagnetic materials, and clutter---all without site visits or calibration. Across three sites spanning 3.6 to 28 GHz, KODAMA's uncalibrated predictions achieve single-digit RMSE, reducing point-to-point error by up to 5.35 dB over automated baselines and coming within 0.22 dB of a measurement-calibrated, hand-built RFDT.
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