Representations in design computing through 3-D deep generative models
| dc.authorid | 0000-0002-9237-9569 | |
| dc.authorid | 0000-0002-0116-8863 | |
| dc.contributor.author | Cakmak, Basak | |
| dc.contributor.author | Ongun, Cihan | |
| dc.date.accessioned | 2026-04-04T18:55:37Z | |
| dc.date.available | 2026-04-04T18:55:37Z | |
| dc.date.issued | 2024 | |
| dc.department | İstanbul Bilgi Üniversitesi | |
| dc.description.abstract | This paper aims to explore alternative representations of the physical architecture using its real-world sensory data through artificial neural networks (ANNs). In the project developed for this research, a detailed 3-D point cloud model is produced by scanning a physical structure with LiDAR. Then, point cloud data and mesh models are divided into parts according to architectural references and part-whole relationships with various techniques to create datasets. A deep learning model is trained using these datasets, and new 3-D models produced by deep generative models are examined. These new 3-D models, which are embodied in different representations, such as point clouds, mesh models, and bounding boxes, are used as a design vocabulary, and combinatorial formations are generated from them. | |
| dc.identifier.doi | 10.1017/S0890060424000106 | |
| dc.identifier.doi | 10.1017/S0890060424000106 | |
| dc.identifier.issn | 0890-0604 | |
| dc.identifier.issn | 1469-1760 | |
| dc.identifier.scopus | 2-s2.0-85212036673 | |
| dc.identifier.scopusquality | Q2 | |
| dc.identifier.uri | https://doi.org/10.1017/S0890060424000106 | |
| dc.identifier.uri | https://hdl.handle.net/11411/10491 | |
| dc.identifier.volume | 38 | |
| dc.identifier.wos | WOS:001372824800001 | |
| dc.identifier.wosquality | Q2 | |
| dc.indekslendigikaynak | Web of Science | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Cambridge Univ Press | |
| dc.relation.ispartof | Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/openAccess | |
| dc.snmz | KA_WoS_20260402 | |
| dc.snmz | KA_Scopus_20260402 | |
| dc.subject | Deep Learning | |
| dc.subject | 3-D Deep Generative Models | |
| dc.subject | Point Cloud | |
| dc.subject | Computational Design | |
| dc.title | Representations in design computing through 3-D deep generative models | |
| dc.type | Article |











