| id |
caadria2024_438 |
| authors |
Lin, Ying and Ye, Fei |
| year |
2024 |
| title |
Architectural Generative Model Evaluation Methods: Image Quality Assessment Metrics and Expert-Based Approach |
| doi |
https://doi.org/10.52842/conf.caadria.2024.1.079
|
| 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. 79–88 |
| summary |
The feasibility of using machine learning methods to generative architectural design solutions has been widely recognized as an effective in enhancing innovation, diversity, and efficiency of solutions. However, in generative design methods, the accuracy and quality of design results often rely on empirical evaluation of expert, which is challenging to evaluate and quantify by unified standards. This paper proposes a comprehensive method for evaluating model performance in architectural design tasks. The evaluation is based on computational criteria (i.e., FID, IS, SIMM indicators) and expert system criteria. The computational metrics will measure the distance, diversity, and similarity between the feature vectors of the real image and the generated image. In contrast, the expert criteria will measure the accuracy, intentionality, and rationality of the layout scheme. This study applies this framework to evaluate three widely used generative models in architectural design: GANs, Diffusion Models, and VAE. The framework also guides the optimization of generative models in architectural applications and assists architects in validating generative outcomes with more efficient workflows. |
| keywords |
Artificial Intelligence, Architectural, Generative Design Methods, Model Evaluation |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (3,514,535 bytes) |
| references |
Content-type: text/plain
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Ali, A. K., & Lee, O. J. (2023)
 Facade style mixing using artificial intelligence for urban infill
, Architecture, 3(2), 258-269
|
|
|
|
|
Chai, S., Zhuang, L., & Yan, F. (2023)
LayoutDM: Transformer-based Diffusion Model for Layout Generation
, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 18349-18358)
|
|
|
|
|
Chaillou, S. (2020)
Archigan: Artificial intelligence x architecture
, Architectural Intelligence: Selected Papers from the 1st International Conference on Computational Design and Robotic Fabrication (CDRF 2019) (pp. 117-127). Singapore: Springer Nature Singapore. association for computer aided design in architecture, Mexico City, Mexico (pp. 18-20)
|
|
|
|
|
Dhariwal, P., & Nichol, A. (2021)
Diffusion models beat gans on image synthesis
, Advances in neural information processing systems, 34, 8780-8794
|
|
|
|
|
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014)
Generative adversarial nets
, Advances in neural information processing systems, 27
|
|
|
|
|
Guida, G. E. O. R. G. E. (2023)
Multimodal Architecture: Applications of Language in a Machine Learning Aided Design Process
, HUMAN-CENTRIC-Proceedings of the 28th CAADRIA Conference. Ahmedabad (pp. 18-24)
|
|
|
|
|
Ho, J., Jain, A., & Abbeel, P. (2020)
Denoising diffusion probabilistic models
, Advances in neural information processing systems, 33, 6840-6851
|
|
|
|
|
Huang, W., & Zheng, H. (2018)
Architectural drawings recognition and generation through machine learning
, Proceedings of the 38th annual conference of the
|
|
|
|
|
Jia, M. (2021)
Daylight prediction using Gan: General workflow
, tool development and case study on Manhattan, New York
|
|
|
|
|
Meng, S. (2022)
Exploring in the latent space of design: A method of plausible building facades images generation, properties control and model explanation base on stylegan2
, Proceedings of the 2021 DigitalFUTURES: The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021) 3 (pp. 55-68). Springer Singapore
|
|
|
|
|
Pang, H. E., & Biljecki, F. (2022)
3D building reconstruction from single street view images using deep learning
, International Journal of Applied Earth Observation and Geoinformation, 112, 102859
|
|
|
|
|
Sun, C., Zhou, Y., & Han, Y. (2022)
Automatic generation of architecture facade for historical urban renovation using generative adversarial network
, Building and Environment, 212, 108781
|
|
|
|
|
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017)
Auto-encoding variational bayes
, Advances in neural information processing systems, 30
|
|
|
|
|
Wang, B., Zhang, S., Zhang, J., & Cai, Z. (2023)
Architectural style classification based on CNN and channel-spatial attention
, Signal, Image and Video Processing, 17(1), 99-107
|
|
|
|
|
YOUSIF, S., & BOLOJAN, D. (2022)
Deep learning-based surrogate modeling for performance-driven generative design systems
, Proc of the 27th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), CAADRIA (pp. 363-372)
|
|
|
|
|
Zhang, H. (2019)
3D model generation on architectural plan and section training through machine learning
, Technologies, 7(4), 82
|
|
|
|
| last changed |
2024/11/17 22:05 |
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