CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

PDF papers
References
id caadria2024_386
authors Liang, Jiadong, Zhong, Ximing and Koh, Immanuel
year 2024
title Bridging Bim and AI: A Graph-Bim Encoding Approach for Detailed 3D Layout Generation Using Variational Graph Autoencoder
source Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 1, pp. 221–230
doi https://doi.org/10.52842/conf.caadria.2024.1.221
summary Building Information Modelling (BIM) data provides an abundant source with hierarchical and detailed information on architectural elements. Nevertheless, transforming BIM data into an understandable format for AI to learn and generate controllable and detailed three-dimensional (3D) models remains a significant research challenge. This paper explores an encoding approach for converting BIM data into graph-structured data for AI to learn 3D models, which we define as Graph-BIM encoding. We employ the graph reconstruction capabilities of a Variational Graph Autoencoder (VGAE) for the unsupervised learning of BIM data to identify a suitable encoding method. VGAE's graph generation capabilities also reason for spatial layouts. Results demonstrate that VGAE can reconstruct BIM 3D models with high accuracy, and can reason the entire spatial layout from partial layout information detailed with architectural components. The primary contribution of this research is to provide a novel encoding approach for bridging AI and BIM encoding. The Graph-BIM encoding method enables low-cost, self-supervised learning of diverse BIM data, capable of learning and understanding the complex relationships between architectural elements. Graph-BIM provides foundational encoding for training general-purpose AI models for 3D generation.
keywords BIM, graph-structured, encoding approach, VGAE, graph reconstruction and generation
series CAADRIA
email
full text file.pdf (7,734,477 bytes)
references Content-type: text/plain
Details Citation Select
100%; open Bassir, D., Lodge, H., Chang, H., Majak, J., & Chen, G. (2023) Find in CUMINCAD Application of artificial intelligence and machine learning for BIM: Review , International Journal for Simulation and Multidisciplinary Design Optimization, 14, 5. https://doi.org/10.1051/smdo/2023005

100%; open Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2022) Find in CUMINCAD Semi-supervised classification with graph convolutional networks , Communications of the ACM, 65(1), 99-106. https://doi.org/10.1145/3503250

100%; open Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020) Find in CUMINCAD House-gan: Relational generative adversarial networks for graph-constrained house layout generation , Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part I 16 (pp. 162-177). Springer International Publishing

100%; open Simonovsky, M., & Komodakis, N. (2018) Find in CUMINCAD Masked label prediction: Unified message passing model for semi-supervised classification , Artificial Neural Networks and Machine Learning-ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I 27 (pp. 412-422)

100%; open Tang, S., Li, X., Zheng, X., Wu, B., Wang, W., & Zhang, Y. (2022) Find in CUMINCAD BIM generation from 3D point clouds by combining 3D deep learning and improved morphological approach , Automation in Construction, 141, 104422. https://doi.org/10.1016/j.autcon.2022.104422

100%; open Winter, R., Noé, F., & Clevert, D. A. (2021) Find in CUMINCAD Permutation-invariant variational autoencoder for graph-level representation learning , Advances in Neural Information Processing Systems, 34, 9559-9573. http://arxiv.org/abs/2104.09856

100%; open Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2018) Find in CUMINCAD How Powerful are Graph Neural Networks? https://doi , org/10.48550/ARXIV.1810.00826

100%; open Zhao, P., Fei, Y., Huang, Y., Feng, Y., Liao, W., & Lu, X. (2023) Find in CUMINCAD Design-condition-informed shear wall layout design based on graph neural networks , Advanced Engineering Informatics, 58, 102190. https://doi.org/10.1016/j.aei.2023.102190

100%; open Zhong, X., Koh, I., & Fricker, P. (2023) Find in CUMINCAD Building-GNN: Exploring a co-design framework , eCAADe 2023: Digital Design Reconsidered

last changed 2024/11/17 22:05
pick and add to favorite papersHOMELOGIN (you are user _anon_909216 from group guest) CUMINCAD Papers Powered by SciX Open Publishing Services 1.002