ARTIFICIAL INTELLIGENCE–BASED 3D MODEL GENERATION FOR ARCHITECTURAL DESIGN: A REVIEW OF CURRENT APPROACHES
Fuente:
Zenodo
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Recurso digital |
| Pubblicazione: |
Zenodo
2025
|
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866902308911054848 |
|---|---|
| author | Zafar Matniyazov Jurat Tajibaev Samidullo Elmurodov Nizomjon Buronov Zilola Rakhmatillaeva |
| author_facet | Zafar Matniyazov Jurat Tajibaev Samidullo Elmurodov Nizomjon Buronov Zilola Rakhmatillaeva |
| contents | <p><em><span lang="EN-US">The advancement of deep learning methods and generative artificial intelligence opens new opportunities for the automated generation of three-dimensional building models. Contemporary research aims to generate entire buildings—from massing to facades and floor plans—utilizing technologies such as GANs, diffusion models, transformers, and LLMs, as well as 3D neural networks (VoxelNet, 3D-GAN) and rule-based systems (shape grammars). This review examines key approaches and tools, including their integration with platforms like Rhino/Grasshopper, Revit, Blender, and others. Emphasis is placed on technical aspects (form generation, parameterization, automation, variability), while ethical and legal issues remain outside the scope of this study. The paper presents method comparisons (including a comparative table) and discusses the limitations and prospects for the further development of architectural 3D model generation.</span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17934870 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ARTIFICIAL INTELLIGENCE–BASED 3D MODEL GENERATION FOR ARCHITECTURAL DESIGN: A REVIEW OF CURRENT APPROACHES Zafar Matniyazov Jurat Tajibaev Samidullo Elmurodov Nizomjon Buronov Zilola Rakhmatillaeva <p><em><span lang="EN-US">The advancement of deep learning methods and generative artificial intelligence opens new opportunities for the automated generation of three-dimensional building models. Contemporary research aims to generate entire buildings—from massing to facades and floor plans—utilizing technologies such as GANs, diffusion models, transformers, and LLMs, as well as 3D neural networks (VoxelNet, 3D-GAN) and rule-based systems (shape grammars). This review examines key approaches and tools, including their integration with platforms like Rhino/Grasshopper, Revit, Blender, and others. Emphasis is placed on technical aspects (form generation, parameterization, automation, variability), while ethical and legal issues remain outside the scope of this study. The paper presents method comparisons (including a comparative table) and discusses the limitations and prospects for the further development of architectural 3D model generation.</span></em></p> |
| title | ARTIFICIAL INTELLIGENCE–BASED 3D MODEL GENERATION FOR ARCHITECTURAL DESIGN: A REVIEW OF CURRENT APPROACHES |
| url | https://doi.org/10.5281/zenodo.17934870 |