A Scan-To-BIM Approach For Renovating Existing Building Rooms Using Surface Reconstruction and Deep Learning

Since global energy consumption is a critical issue and the building sector plays a significant role in high energy demand, renovating existing buildings is crucial. Building Information Modelling (BIM) represents the physical and functional characteristics of a building or structure with detailed information. It allows all the architecture, engineering, and construction (AEC) industry stakeholders to work collaboratively. As-built BIM models reflect the modifications of existing conditions of buildings and fully automated generation of as-built BIM models remains a major challenge. The Scan-to-BIM process is widely used for the renovation and documentation of existing buildings. This process includes capturing the physical conditions of a building or structure using 3D laser scanning technology and converting it into a BIM model. Originally, 3D laser scanners had a very high cost, however, free 3D scanning applications that use Light Detection and Ranging (LiDAR) technology can be easily compatible with mobile phones or tablets nowadays. This paper proposes to contribute to renovating existing buildings by developing a new approach to scan-to-BIM combining surface reconstruction from point cloud data and object detection with deep learning. A 3D free scanning application generated the point cloud data of the interior room of the building and the surface model was reconstructed. Moreover, the location of the air terminals on the ceiling was investigated by developing the object detection model with deep learning and installing the air terminals on the surface reconstructed model. The finalized surface model with the air terminals was exported to Industry Foundation Classes (IFC) format. The reconstructed IFC model developed in this research can be used for Computational Fluid Dynamics (CFD) analysis with appropriate property sets.