A Geometric-Relational Deep Learning Framework for BIM Object Classification

TL;DR

This paper introduces a novel geometric-relational deep learning framework for BIM object classification that leverages both shape and relational information, improving accuracy and interoperability in BIM applications.

Contribution

The paper proposes a two-branch deep learning framework that incorporates relational data into geometric classification, along with a new BIM object dataset IFCNet++.

Findings

Relational features improve classification accuracy.

Framework adapts to various geometric methods.

Enhanced BIM model checking efficiency.

Abstract

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.