# IFCNet: A Benchmark Dataset for IFC Entity Classification

arXiv: https://arxiv.org/abs/2106.09712 · HTML: https://mlsanity.com/paper/2106.09712/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 17 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [MeshNet: Mesh Neural Network for 3D Shape Representation](https://mlsanity.com/paper/1811.11424/) (2018)

## Built on by (most cited first, 4 of 4)

- [SFS-A68: a dataset for the segmentation of space functions in apartment buildings](https://mlsanity.com/paper/2209.09094/) (2022)
- [A Geometric-Relational Deep Learning Framework for BIM Object Classification](https://mlsanity.com/paper/2212.00942/) (2022)
- [DC3DO: Diffusion Classifier for 3D Objects](https://mlsanity.com/paper/2408.06693/) (2024)
- [From Geometric Labels to Semantic Understanding of Indoor Building Components Using Multimodal Large Language Models](https://mlsanity.com/paper/2607.03661/) (2026)

## Abstract (arXiv)

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.