| id |
ecaade2024_235 |
| authors |
Mueller, Lisa-Marie; Andriotis, Charalampos; Turrin, Michela |
| year |
2024 |
| title |
Data and Parameterization Requirements for 3D Generative Deep Learning Models |
| 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. 615–624 |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.615
|
| summary |
It is now within reach to use generative artificial intelligence (AI) to autonomously generate full building geometries. However, existing literature utilizing 3D data has focused to a limited degree on architecture and engineering disciplines. A critical first step to expanding the use of generative deep learning models in generative design research is making training data available. This study investigates 3D building model data characteristics that make it suitable for generative AI applications. Key data set attributes are identified through a systematic review of the object-containing datasets currently used to train state-of-the-art 3D GANs. These requirements are then compared to attributes of existing available building datasets. This comparison shows that publicly available data sets of 3D building models lack essential characteristics for generative deep learning. Features that make these building models inadequate for the task include but are not limited to, their mesh formats, low resolution and levels of detail, and inclusion of irrelevant geometry. To achieve the desired properties in this work, necessary transformations of the data are incorporated into a tailored preprocessing pipeline. The pipeline is applied to an existing dataset that contains 3D models of single-family homes. The transformed dataset is tested within state-of-the-art GAN models to assess training performance and document future data requirements for applying deep generative design to buildings. Our experiments show promise for the impact that architectural datasets can make on deep learning applications within the discipline. It also highlights the need for additional 3D building model data to increase the diversity and robustness of new designs. |
| keywords |
Generative Deep Learning, Data Sets, Generative Adversarial Networks |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (755,369 bytes) |
| references |
Content-type: text/plain
|
Biljecki, F., Ledoux, H., and Stoter, J. (2016)
 An improved LOD specification for 3D building models
, Computers, Environment and Urban Systems, pages 25-37
|
|
|
|
|
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009)
Imagenet: A large-scale hierarchical image database
, 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255)
|
|
|
|
|
Du, Z., Shen, H., Li, X., & Wang, M. (2022)
3D building fabrication with geometry and texture coordination via hybrid GAN
, Journal of Ambient Intelligence and Humanized Computing, 13(11), 5177-5188
|
|
|
|
|
Feng, Q., Guo, C., Benitez-Quiroz, F., and Martinez, A.. (2021)
When Do GANs Replicate? On the Choice of Dataset Size
, 2021 IEEE/CVF ICCV, 6681-90, Montreal, QC, Canada: IEEE
|
|
|
|
|
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014)
Generative Adversarial Networks
, Advances in Neural Information Processing Systems 3 (June)
|
|
|
|
|
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T. (2020)
Training Generative Adversarial Networks with Limited Data
, Advances in Neural Information Processing Systems, 33:12104-14. Curran Associates, Inc
|
|
|
|
|
Li, X., Zhang, Q., Kang, D., Cheng, W., Gao, Y., Zhang, J., Liang, Z., Liao, J., Cao, Y.-P., & Shan, Y. (2024)
Advances in 3D Generation: A Survey
, (arXiv:2401.17807). arXiv
|
|
|
|
|
Malah, M., Agaba, R., & Abbas, F. (2024)
Generating 3D Reconstructions Using Generative Models
, Z. Lyu (Ed.), Applications of Generative AI (pp. 403-419). Springer International Publishing
|
|
|
|
|
Mueller, L.M., Andriotis, C., Turrin, M. (2024)
Using Generative Adversarial Networks to Create 3D Building Geometries: 3DBuildingGAN
, eCAADe 2024
|
|
|
|
|
Newton, D. (2019)
Generative Deep Learning in Architectural Design
, Technology|Architecture + Design 3 (2): 176-89
|
|
|
|
|
Park, K., Ergan, S., & Feng, C. (2024)
Quality assessment of residential layout designs generated by relational Generative Adversarial Networks (GANs)
, Automation in Construction, 158, 105243
|
|
|
|
|
Schwab, B. and Wysocki, O. (2021)
Ingolstadt 3D City Model [Online]
, At: https://github.com/savenow/lod3-road-space-models (Accessed December 2022)
|
|
|
|
|
Selvaraju, P., Nabail, M., Loizou, M., Maslioukova, M., Averkiou, M., Andreou, A., Chaudhuri, S., and Kalogerakis, E. (2021)
Buildingnet: Learning to label 3d buildings
, IEEE/CVF ICCV
|
|
|
|
|
Smith, E. and Meger, D. (2017)
Improved Adversarial Systems for 3D Object Generation and Reconstruction
, arXiv:1707.09557
|
|
|
|
|
Wang, Y., Wu, C., Herranz, L., van de Weijer, J., Gonzalez-Garcia, A., and Raducanu, B. (2018)
Transferring GANs: Generating Images from Limited Data
, ECCV 2018
|
|
|
|
|
Weber, R. E., Mueller, C., & Reinhart, C. (2022)
Automated floorplan generation in architectural design: A review of methods and applications
, Automation in Construction, 140, 104385
|
|
|
|
|
Wu, A. N., Stouffs, R., & Biljecki, F. (2022)
Generative Adversarial Networks in the Built Environment: A Comprehensive Review of the Application of GANs across Data Types and Scales
, Building and Environment, 223(109477)
|
|
|
|
|
Xiang, Y., Kim, W., Chen, W., Ji, J., Choy, C., Su, H., Mottaghi, R., Guibas, L., and Savarese, S. (2016)
Objectnet3d: A large scale database for 3d object recognition
, European Conference Computer Vision (ECCV)
|
|
|
|
|
Xiang, Y., Mottaghi, R., and Savarese, S. (2014)
Beyond pascal: A benchmark for 3d object detection in the wild
, IEEE Winter Conference on Applications of Computer Vision (WACV)
|
|
|
|
| last changed |
2024/11/17 22:05 |
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