  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.
