| id |
ecaade2024_198 |
| authors |
Liang, Jiadong; Zhong, Ximing; Koh, Immanuel |
| year |
2024 |
| title |
Building-VGAE: Generating 3D detailing and layered building models from simple geometry |
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 625–634 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.625
|
| summary |
In the current field of AI-assisted architectural design, deep learning models primarily focus on simulating the highly detailed final models designed by human architects. However, in practical design tasks, the final model demands a high level of detail and clear layered classification information for building components. This presents a more significant challenge. We propose a three-dimensional(3D) building generation framework—Building-VGAE, based on Variational Graph Autoencoder (VGAE). Building-VGAE can generate 3D models with detailed building components and layered structure information from end to end, according to design constraints and building volumes. Building-VGAE’s experiment involves transforming 27,965 Housegan data into 3D data represented as graph-structured. The VGAE model then learns the data features and predicts the building component categories to which nodes and edges belong in the experiment. The results demonstrate that the framework can precisely reconstruct and predict building layouts that comply with design constraints and enable unified editing of building components of the same category. Building-VGAE contributes to its ability to learn the generative relationship from design constraints and building volumes to complex high-detail models compared to existing AI generative models. It also possesses prediction and editing capabilities based on the layered classification information of building components. This framework has the potential to position AI as a design partner for human architects, offering end-to-end 3D generative intelligence. |
| keywords |
Variational Graph Auto-Encoder, 3D Spatial Grid Structure, Detailed Building Components, Layered Structure, Graph Reconstruction and Generation |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (8,733,395 bytes) |
| references |
Content-type: text/plain
|
Avetisyan, A., Xie, C., Howard-Jenkins, H., Yang, T. Y., Aroudj, S., Patra, S., ... & Balntas, V. (2024)
 SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model
, arXiv preprint arXiv:2403.13064
|
|
|
|
|
Chang, K.-H. and Cheng, C.-Y. (2020)
Learning to simulate and design for structural engineering
, arXiv (Cornell University) https://arxiv.org/pdf/2003.09103.pdf
|
|
|
|
|
Chang, K.-H. et al. (2021)
Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation
, 2021 IEEE/CVF International Conference on Computer Vision (ICCV) [Preprint].https://doi.org/10.1109/iccv48922.2021.01174
|
|
|
|
|
Fang, H., & Lafarge, F. (2020)
Connect-and-slice: a hybrid approach for reconstructing 3d objects
, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13490-13498)
|
|
|
|
|
Hamilton, W.L., Ying, R. and Leskovec, J. (2017)
Inductive representation learning on large graphs
, arXiv (Cornell University) [Preprint]. https://arxiv.org/pdf/1706.02216.pdf
|
|
|
|
|
Hübner, P., Weinmann, M., Wursthorn, S., & Hinz, S. (2021)
Automatic voxel-based 3D indoor reconstruction and room partitioning from triangle meshes
, ISPRS Journal of Photogrammetry and Remote Sensing, 181, 254-278
|
|
|
|
|
Jiadong Liang, Ximing Zhong, and Immanuel Koh. (2024)
BRIDGING BIM AND AI: A Graph-BIM Encoding Approach For Detailed 3D Layout Generation Using Variational Graph Autoencoder
, Proceedings of the 29th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2024, Volume 2, 19-28. © 2024 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong
|
|
|
|
|
Kipf, T. N., & Welling, M. (2016)
Variational Graph Auto-Encoders
, arXiv:1611.07308
|
|
|
|
|
Kipf, T. N., & Welling, M. (2016)
Semi-supervised classification with graph convolutional networks
, arXiv:1609.02907
|
|
|
|
|
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2022)
NeRF: Representing scenes as neural radiance fields for view synthesis
, Communications of the ACM, 65(1), 99-106, https://doi.org/10.1145/3503250
|
|
|
|
|
Nauata, N., Chang, K. H., Cheng, C. Y., Mori, G., & Furukawa, Y. (2020)
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
|
|
|
|
|
Shen, T., Gao, J., Yin, K., Liu, M. Y., & Fidler, S. (2021)
Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis
, Advances in Neural Information Processing Systems, 34, 6087-6101
|
|
|
|
|
Shi, Y., Huang, Z., Feng, S., Zhong, H., Wang, W., & Sun, Y. (2020)
Masked label prediction: Unified message passing model for semi-supervised classification
, arXiv preprint arXiv:2009.03509. https://doi.org/10.48550/ARXIV.2009.03509
|
|
|
|
|
Simonovsky, M., & Komodakis, N. (2018)
Graphvae: Towards generation of small graphs using variational autoencoders
, 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)
|
|
|
|
|
Veličković, P. et al. (2018)
Graph attention networks
, arXiv (Cornell University) https://doi.org/10.17863/cam.48429
|
|
|
|
|
Winter, R., Noé, F., & Clevert, D. A. (2021)
Permutation-invariant variational autoencoder for graph-level representation learning
, Advances in Neural Information Processing Systems, 34, 9559-9573, http://arxiv.org/abs/2104.09856
|
|
|
|
|
Zhao, L., Cai, D., Sheng, L. & Xu, D. (2021)
3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds
, 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Available at: https://doi.org/10.1109/ICCV48922.2021.00292
|
|
|
|
|
Zhao, P., Fei, Y., Huang, Y., Feng, Y., Liao, W., & Lu, X. (2023)
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
|
|
|
|
|
Zhiqin Chen, Andrea Tagliasacchi, and Hao Zhang. (2020)
Bsp-net: Generating compact meshes via binary space partitioning.
, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 45-54
|
|
|
|
| last changed |
2024/11/17 22:05 |
|
