Automatic classification of wall and door BIM element subtypes using 3D geometric deep neural networks

With the growing adoption of Building Information Modeling (BIM), specialized applications have been developed to perform domain-specific analyses. These applications need tailored information with respect to a BIM model element's attributes and relationships. In particular, architectural elements need further qualification concerning their geometric and functional ‘subtypes’ to support exact simulations and compliance checks. BIM and its underlying data schema, the Industry Foundation Classes (IFC), provide a rich representation with which to exchange semantic entity and relationship data. However, subtypes for individual elements are not represented by default and often require manual designation, leaving it vulnerable to errors and omissions. Existing research to enrich the semantics of IFC model entities employed domain-specific rule sets that scrutinize their legitimacy and modify them, if and when necessary. However, such an approach is limited in their scalability and comprehensibility. This study explored the use of 3D geometric deep neural networks originating from computer vision research. Specifically, Multi-view CNN(MVCNN) and PointNet were investigated to determine their applicability in extracting unique features of door (IfcDoor) and wall (IfcWall) element subtypes, and in turn be leveraged to automate subtype classifications. Test results indicated MVCNN as having the best prediction performance, while PointNet's accuracy was hampered by resolution loss due to selective use of point cloud data. The research confirmed deep neural networks as a viable solution to distinguishing BIM element subtypes, the critical factor being their ability to detect subtle differences in local geometries.