| id |
caadria2016_881 |
| authors |
Silvestre, Joaquim; Yasushi Ikeda and Franc?ois Gue?na |
| year |
2016 |
| title |
Artificial Imagination of Architecture with Deep Convolutional Neural Network |
| source |
Living Systems and Micro-Utopias: Towards Continuous Designing, Proceedings of the 21st International Conference on Computer-Aided Architectural Design Research in Asia (CAADRIA 2016) / Melbourne 30 March–2 April 2016, pp. 881-890 |
| doi |
https://doi.org/10.52842/conf.caadria.2016.881
|
| summary |
This paper attempts to determine if an Artificial Intelli- gence system using deep convolutional neural network (ConvNet) will be able to “imagine” architecture. Imagining architecture by means of algorithms can be affiliated to the research field of generative archi- tecture. ConvNet makes it possible to avoid that difficulty by automat- ically extracting and classifying these rules as features from large ex- ample data. Moreover, image-base rendering algorithms can manipu- late those abstract rules encoded in the ConvNet. From these rules and without constructing a prior 3D model, these algorithms can generate perspective of an architectural image. To conclude, establishing shape grammar with this automated system opens prospects for generative architecture with image-base rendering algorithms. |
| keywords |
Machine learning; convolutional neural network; generative design; image-based rendering |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (24,587,129 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:56 |
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