  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.
