Fabian Kaufmann

• As Building Information Modelling (BIM) continues to gain widespread adoption in the industry, its application in existing building works becomes increasingly relevant. To effectively apply BIM in alteration, renovation, and demolition projects, a model of the existing structure is crucial. While traditional methods relied on manual measurements and conventional surveying equipment, modern advancements have introduced efficient reality-capturing devices that combine Terrestrial Laser Scanners (TLS) and Structure from Motion (SfM) to rapidly acquire 3D data of entire buildings. However, the point clouds generated by these devices represent buildings as a dense collection of points, each covering physical objects with millions of data points. Since point clouds are unstructured data, they do not provide any further information about the building. Hence, it is essential to convert this data into Building Information Models (BIMs). Manually performing this conversion requires substantial effort, making the automation of the scan-to-BIM process critical for integrating existing structures into BIM workflows. This work proposes a methodology for automatically generating BIMs from point clouds. The ScaleBIM framework consists of three main steps: (i) Semantic segmentation, where each point in the point cloud is assigned a semantic label, typically corresponding to a building element class. (ii) Geometry reconstruction, utilizing topology-aware refinement procedures to ensure the creation of a watertight BIM model that is functional for other use cases. (iii) Delivering the model in an open data format using open-source BIM authoring tools to maximize interoperability. These three steps are integrated into a comprehensive pipeline and rigorously tested on two datasets with varying characteristics. The pipeline reconstructs walls, doors and columns and delivers the respective BIM objects including the building component class and geometric shape representation. Since the semantic segmentation procedure involves machine learning, annotated training data is necessary. To address this need, this work provides annotation guidelines and semiautomated annotation procedures, along with two datasets comprising 2.8 billion annotated points and manually created reference BIMs. Additionally, efficient and robust filtering, downsampling and geometry reconstruction techniques have been either extended or newly developed. For both geometry reconstruction and open BIM authoring, this work introduces algorithms and procedures, including the two Python modules pystruct3d and openbimxd. Along with pipeline integration and evaluation, new metrics have been developed to provide a more realistic quantification of reconstruction accuracy. Finally, concluding remarks highlight perspectives for future research.