Creating geometric digital twins (gDT) for as-built roads still faces many challenges, such as low automation level and accuracy, limited asset types and shapes, and reliance on engineering experience. A novel scan-to-building information modeling (scan-to-BIM) framework is proposed for automatic road gDT creation based on semantically labeled point cloud data (PCD), which considers six asset types: Road Surface, Road Side (Slope), Road Lane (Marking), Road Sign, Road Light, and Guardrail. The framework first segments the semantic PCD into spatially independent instances or parts, then extracts the sectional polygon contours as their representative geometric information, stored in JavaScript Object Notation (JSON) files using a new data structure. Primitive gDTs are finally created from JSON files using corresponding conversion algorithms. The proposed method achieves an average distance error of 1.46 centimeters and a processing speed of 6.29 meters per second on six real-world road segments with a total length of 1,200 meters.

翻译：为既有道路创建几何数字孪生体仍面临诸多挑战，例如自动化水平和精度低、资产类型和形状有限以及对工程经验的依赖。本文提出了一种新颖的扫描到建筑信息建模框架，用于基于语义标注的点云数据自动创建道路几何数字孪生体。该框架考虑了六种资产类型：路面、路侧（边坡）、车道线（标线）、交通标志、路灯和护栏。该框架首先将语义点云数据分割为空间上独立的实例或部件，然后提取其截面多边形轮廓作为代表性几何信息，并使用一种新的数据结构将其存储在JSON文件中。最终，通过相应的转换算法从JSON文件生成初步的几何数字孪生体。在总长度为1200米的六个真实道路段上，所提方法的平均距离误差为1.46厘米，处理速度为每秒6.29米。