IFCNet: A Benchmark Dataset for IFC Entity Classification
This provides a dataset for researchers in the AECO industry to improve interoperability in BIM, but it is incremental as it focuses on data creation rather than novel methods.
The authors tackled the lack of large datasets for training machine learning models in BIM by introducing IFCNet, a benchmark dataset of IFC files with geometric and semantic information, and demonstrated that three deep learning models achieved good classification performance using only geometric data.
Enhancing interoperability and information exchange between domain-specific software products for BIM is an important aspect in the Architecture, Engineering, Construction and Operations industry. Recent research started investigating methods from the areas of machine and deep learning for semantic enrichment of BIM models. However, training and evaluation of these machine learning algorithms requires sufficiently large and comprehensive datasets. This work presents IFCNet, a dataset of single-entity IFC files spanning a broad range of IFC classes containing both geometric and semantic information. Using only the geometric information of objects, the experiments show that three different deep learning models are able to achieve good classification performance.