From Enriched Point Cloud to Structural and MEP Models: An Automated Approach to Create Semantic-Geometric Models for Industrial Facilities

While helpful for engineering applications, digital models representing the as-is status of the built environment are rarely available and costly to create using conventional methods. Commonly, editable and preferably parametric model geometries are preferred over less easy-to-process, triangulated meshes where possible; additional semantic information beyond the geometry is required in almost any case. We propose an end-to-end method starting from conventional laser-scanned point clouds including RGB color information: the captured data is processed using semantic and instance segmentation and model fitting first to identify semantic clusters and object instances, and then selected structural and MEP elements are reconstructed using geometric primitives and procedural geometric operations such as sweeps to generate meaningful, ready-to-use models. We describe all steps individually, along with a prototypical implementation in which we use state-of-the-art segmentation and reconstruction methods on a real-world dataset collected by the authors. Intermediate and final results are showcased and critically discussed.