发表机构
Nokia Bell Labs; University of York（诺基亚贝尔实验室; 约克大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
KODAMA利用现成地理空间数据自动重建城市级射频数字孪生，无需现场勘测或校准，在3.6-28 GHz频段实现个位数RMSE，误差较自动化基线最多降低5.35 dB。
AI 中文摘要
三维重建通常追求几何保真度或视觉合理性。射频数字孪生（RFDT）则不同，其优劣取决于通信信道在其中是否表现得与真实世界一致。RFDT有望实现站点特定的信道预测，但当前实践迫使人们在粗略的自动化场景与手工构建、经测量校准且每站点需数周构建的模型之间做出选择。我们提出KODAMA，一种自动化流水线，仅凭现成的地理空间数据——航空影像、LiDAR和摄影测量——即可在城市尺度重建可直接用于射线追踪的RFDT：这些数据生成地形和水密建筑网格，而街景影像的曝光加权多视图融合则恢复立面浮雕、电磁材料和杂波——全程无需现场勘测或校准。在跨越3.6至28 GHz的三个站点上，KODAMA未经校准的预测实现了个位数均方根误差（RMSE），将点对点误差相比自动化基线最多降低5.35 dB，并与经测量校准的手工构建RFDT相差仅0.22 dB。
英文摘要
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 façade 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.
