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Communication Dans Un Congrès Année : 2016

Artificial Imagination of Architecture with Deep Convolutional Neural Network

Résumé

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.
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Dates et versions

hal-03690510 , version 1 (08-06-2022)

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Citer

Joaquim Silvestre, Yasushi Ikeda, François Guéna. Artificial Imagination of Architecture with Deep Convolutional Neural Network. CAADRIA 2016: Living Systems and Micro-Utopias - Towards Continuous Designing, Mar 2016, Melbourne, Australia. pp.881-890, ⟨10.52842/conf.caadria.2016.881⟩. ⟨hal-03690510⟩

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