| id |
ecaade2023_250 |
| authors |
Sebestyen, Adam, Özdenizci, Ozan, Hirschberg, Urs and Legenstein, Robert |
| year |
2023 |
| title |
Generating Conceptual Architectural 3D Geometries with Denoising Diffusion Models |
| source |
Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 451460 |
| doi |
https://doi.org/10.52842/conf.ecaade.2023.2.451
|
| summary |
Generative deep learning diffusion models have been attracting mainstream attention in the field of 2D image generation. We propose a prototype which brings a diffusion network into the third dimension, with the purpose of generating geometries for conceptual design. We explore the possibilities of generating 3D datasets, using parametric design to overcome the problem of the current lack of available architectural 3D data suitable for training neural networks. Furthermore, we propose a data representation based on volumetric density grids which is applicable to train diffusion networks. Our early prototype demonstrates the viability of the approach and suggests future options to develop deep learning generative 3D tools for architectural design. |
| keywords |
Artificial Intelligence, Generative Deep Learning, Neural Networks, Diffusion Models, Parametric Design, 3D Data Representations |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (4,171,484 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/12/10 10:49 |
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