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.

该研究论文介绍了一种基于深度学习的两支几何-关联框架，以解决建筑信息模型中物体类型在数据交换或在不同域的软件中创建模型时所存在的互通性问题。作者还提供了IFCNet++ BIM对象数据集，并表明这种机器学习方法明显突破了以往基于形状信息的分类方法，增加了一种基于关系信息的算法，通过改善分类性能来降低对人工审核建筑模型的工作量并提高此类模型的实用性。

基于几何关系的深度学习模型在BIM对象分类中的应用