用手机激光扫描数据提取路面材料的辐射指纹，实现厘米级城市数字孪生。
Radiometric fingerprinting of object surfaces using mobile laser scanning and semantic 3D road space models
- 通过多源激光扫描数据聚类同一语义物体的反射信号，构建辐射指纹。
- 在4次扫描、5种传感器下，关联3.1亿个光束与6368个物体。
- 适合城市建模、智能驾驶和材料分析研究者使用。
尽管语义三维城市模型已广泛可用且细节日益丰富，但材料信息仍基本未被利用。若能结构化表示材料及其物理属性，将显著拓展城市数字孪生的应用范围与分析能力。同时，城市及街道空间的重复移动激光扫描产生了大量受表面材质影响的观测数据。为此，我们提出通过聚合同一语义对象在不同距离、入射角、环境条件、传感器和扫描周期下的激光雷达（LiDAR）观测，构建物体表面的辐射指纹。研究基于奥迪自动驾驶数据集（A2D2）车辆在4次扫描中使用5种激光雷达传感器采集的3.124亿个独立光束，自动关联至语义3D城市模型中的6368个个体对象。该模型覆盖四个城区街道，符合城市地理标记模型（CityGML 3.0）标准，达到厘米级精度，支持细粒度对象区分。提取的辐射指纹揭示了同类表面的重复性模式，表明其主导材料特性。语义模型、方法实现及开发的地理数据库系统3DSensorDB已开源：https://github.com/tum-gis/sensordb
原文摘要 · Abstract (English)
Although semantic 3D city models are internationally available and becoming increasingly detailed, the incorporation of material information remains largely untapped. However, a structured representation of materials and their physical properties could substantially broaden the application spectrum and analytical capabilities for urban digital twins. At the same time, the growing number of repeated mobile laser scans of cities and their street spaces yields a wealth of observations influenced by the material characteristics of the corresponding surfaces. To leverage this information, we propose radiometric fingerprints of object surfaces by grouping LiDAR observations reflected from the same semantic object under varying distances, incident angles, environmental conditions, sensors, and scanning campaigns. Our study demonstrates how 312.4 million individual beams acquired across four campaigns using five LiDAR sensors on the Audi Autonomous Driving Dataset (A2D2) vehicle can be automatically associated with 6368 individual objects of the semantic 3D city model. The model comprises a comprehensive and semantic representation of four inner-city streets at Level of Detail (LOD) 3 with centimeter-level accuracy. It is based on the CityGML 3.0 standard and enables fine-grained sub-differentiation of objects. The extracted radiometric fingerprints for object surfaces reveal recurring intra-class patterns that indicate class-dominant materials. The semantic model, the method implementations, and the developed geodatabase solution 3DSensorDB are released under: https://github.com/tum-gis/sensordb
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