dc.creator | Miguel Rodríguez, Jaime de | es |
dc.creator | Villafañe, María Eugenia | es |
dc.creator | Piškorec, Luka | es |
dc.creator | Sancho Caparrini, Fernando | es |
dc.date.accessioned | 2023-10-10T06:36:34Z | |
dc.date.available | 2023-10-10T06:36:34Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Miguel Rodríguez, J.d., Villafañe, M.E., Piškorec, L. y Sancho Caparrini, F. (2020). Generation of geometric interpolations of building types with deep variational autoencoders. Design Science, 6 (e34). https://doi.org/10.1017/dsj.2020.31. | |
dc.identifier.issn | 2053-4701 | es |
dc.identifier.uri | https://hdl.handle.net/11441/149570 | |
dc.description.abstract | This work presents a methodology for the generation of novel 3D objects resembling wireframes of building types. These result from the reconstruction of interpolated locations within the learnt distribution of variational autoencoders (VAEs), a deep generative machine learning model based on neural networks. The data set used features a scheme for geometry representation based on a ‘connectivity map’ that is especially suited to express the wireframe objects that compose it. Additionally, the input samples are generated through ‘parametric augmentation’, a strategy proposed in this study that creates coherent variations among data by enabling a set of parameters to alter representative features on a given building type. In the experiments that are described in this paper, more than 150 k input samples belonging to two building types have been processed during the training of a VAE model. The main contribution of this paper has been to explore parametric augmentation for the generation of large data sets of 3D geometries, showcasing its problems and limitations in the context of neural networks and VAEs. Results show that the generation of interpolated hybrid geometries is a challenging task. Despite the difficulty of the endeavour, promising advances are presented. | es |
dc.format | application/pdf | es |
dc.format.extent | 35 p. | es |
dc.language.iso | eng | es |
dc.publisher | Cambridge University Press | es |
dc.relation.ispartof | Design Science, 6 (e34). | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Artificial intelligence | es |
dc.subject | Artificial neural networks | es |
dc.subject | Computer-aided architectural design | es |
dc.subject | Computer-aided design | es |
dc.subject | Deep generative models | es |
dc.subject | Deep learning | es |
dc.subject | Deep neural networks | es |
dc.subject | Form-finding | es |
dc.subject | Generative design | es |
dc.subject | Procedural design | es |
dc.subject | Structural design | es |
dc.title | Generation of geometric interpolations of building types with deep variational autoencoders | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Estructuras de Edificación e Ingeniería del Terreno | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificial | es |
dc.relation.publisherversion | https://www.cambridge.org/core/services/aop-cambridge-core/content/view/F4899EC122329816CD137503D8118875/S2053470120000311a.pdf/generation-of-geometric-interpolations-of-building-types-with-deep-variational-autoencoders.pdf | es |
dc.identifier.doi | 10.1017/dsj.2020.31 | es |
dc.contributor.group | Universidad de Sevilla. TIC-137: Lógica, Computación e Ingeniería del Conocimiento | es |
dc.journaltitle | Design Science | es |
dc.publication.volumen | 6 | es |
dc.publication.issue | e34 | es |