DEEP LEARNING-BASED ROAD SEGMENTATION OF 3D POINT CLOUDS FOR ASSISTING ROAD ALIGNMENT PARAMETERIZATION

Abstract. The need for transportation infrastructure digitalization is becoming more important, and efficient data collection and processing workflows have to be established and pose a great research challenge. This paper presents a fully automated method for the geometric parametrization of the road alignment from 3D point clouds acquired with a low-cost mobile mapping system. It exploits the Point Transformer Deep Learning architecture in order to segment the 3D point cloud in four different classes, which include road markings. Those markings are then used as a reference to extract the alignment trajectory path, classify its geometries (straight lines, circular arcs, and clothoids) and then parametrize it, extracting data to easily generate alignment data that may follow the standard schema of the Industry Foundation Classes (IFC). Both the deep learning architecture and the geometry parametrization process show promising results to develop automatic workflows that extract precise as-built data of the infrastructure from 3D point clouds.