A Geometric-Relational Deep Learning Framework for BIM Object Classification
This work addresses interoperability issues in Building Information Modeling for applications like scan-to-BIM and code compliance checking, though it is incremental as it builds on existing geometric methods.
The authors tackled the problem of BIM object classification by introducing a two-branch geometric-relational deep learning framework that incorporates relational information, which improved classification performance and reduced manual labor in model checking.
Interoperability issue is a significant problem in Building Information Modeling (BIM). Object type, as a kind of critical semantic information needed in multiple BIM applications like scan-to-BIM and code compliance checking, also suffers when exchanging BIM data or creating models using software of other domains. It can be supplemented using deep learning. Current deep learning methods mainly learn from the shape information of BIM objects for classification, leaving relational information inherent in the BIM context unused. To address this issue, we introduce a two-branch geometric-relational deep learning framework. It boosts previous geometric classification methods with relational information. We also present a BIM object dataset IFCNet++, which contains both geometric and relational information about the objects. Experiments show that our framework can be flexibly adapted to different geometric methods. And relational features do act as a bonus to general geometric learning methods, obviously improving their classification performance, thus reducing the manual labor of checking models and improving the practical value of enriched BIM models.