Automating Built Heritage Modelling for the Integration into 3D City Models

The interest in studies on 3D city models has renewed over time thanks to technological innovations that concern the methods of data acquisition and automatic classification of unstructured point clouds, to model increasingly specialized geometric objects for the growing needs of urban transformation management. Managing geometric and semantic information in cognitive systems, based on standards, increasingly open to the inquiries, analyses, and predictions that today are necessary to implement for future assessments, can digitally twin reality. The problem focused on in this work is the modelling phase, which requires efficient automatisms to make the production phase of geometric objects more sustainable, whether they are then destined to become parametric features in a BIM/CIM environment or 3D geometric objects of a geodatabase. A comprehensive methodology for automating the generation of 3D meshes from point cloud data using Python code, with a focus on practical implementation is presented parallelly to evaluations regarding the generation of the geometric 3D contents of the urban environment representation, depending on whether or not it includes examples of architectural heritage (of all ages). Leveraging the Open3D library and NumPy, the methodology provides different experiences, tests, and related discussions in the field of architectural and urban 3D surface reconstruction and visualization. The paper outlines essential steps including data loading and preparation, meshing strategy selection, data processing using Poisson reconstruction, and mesh exportation and visualization. Additionally, a method for generating multiple Levels of Detail (LoD) meshes is introduced, enhancing the applicability of the approach for city modelling framework applications, including architectural elements of buildings. The presented Python code serves as a practical resource for implementing 3D mesh generation workflows seamlessly.