Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts
In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintainin...
| Main Authors: | , , , , |
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| Format: | Article |
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Multidisciplinary Digital Publishing Institute
2023
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| Online Access: | https://hdl.handle.net/1721.1/151180 |
| _version_ | 1811068244416528384 |
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| author | Tanasra, Hanan Rott Shaham, Tamar Michaeli, Tomer Austern, Guy Barath, Shany |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Tanasra, Hanan Rott Shaham, Tamar Michaeli, Tomer Austern, Guy Barath, Shany |
| author_sort | Tanasra, Hanan |
| collection | MIT |
| description | In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality. The aim of this study was to develop a furnishing method that leverages machine learning as a means for enhancing design processes. A secondary aim was to develop a set of evaluation metrics for assessing the quality of the results generated from such methods, enabling comparisons between the performance of different models. To achieve these aims, floor plans were tagged and assembled into a comprehensive dataset that was then employed for training and evaluating three conditional generative adversarial network models (pix2pix, BicycleGAN, and SPADE) to generate furniture layouts within given room boundaries. Post-processing methods for improving the generated results were also developed. Finally, evaluation criteria that combine measures of architectural design with standard computer vision parameters were devised. Visual architectural analyses of the results confirm that the generated rooms adhere to accepted architectural standards. The numerical results indicate that BicycleGAN outperformed the two other models. Moreover, the overall results demonstrate a machine-learning workflow that can be used to augment existing interior design processes. |
| first_indexed | 2024-09-23T07:53:29Z |
| format | Article |
| id | mit-1721.1/151180 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T07:53:29Z |
| publishDate | 2023 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1511802024-01-31T21:13:02Z Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts Tanasra, Hanan Rott Shaham, Tamar Michaeli, Tomer Austern, Guy Barath, Shany Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory In interior space planning, the furnishing stage usually entails manual iterative processes, including meeting design objectives, incorporating professional input, and optimizing design performance. Machine learning has the potential to automate and improve interior design processes while maintaining creativity and quality. The aim of this study was to develop a furnishing method that leverages machine learning as a means for enhancing design processes. A secondary aim was to develop a set of evaluation metrics for assessing the quality of the results generated from such methods, enabling comparisons between the performance of different models. To achieve these aims, floor plans were tagged and assembled into a comprehensive dataset that was then employed for training and evaluating three conditional generative adversarial network models (pix2pix, BicycleGAN, and SPADE) to generate furniture layouts within given room boundaries. Post-processing methods for improving the generated results were also developed. Finally, evaluation criteria that combine measures of architectural design with standard computer vision parameters were devised. Visual architectural analyses of the results confirm that the generated rooms adhere to accepted architectural standards. The numerical results indicate that BicycleGAN outperformed the two other models. Moreover, the overall results demonstrate a machine-learning workflow that can be used to augment existing interior design processes. 2023-07-28T20:59:58Z 2023-07-28T20:59:58Z 2023-07-14 2023-07-28T12:21:35Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/151180 Buildings 13 (7): 1793 (2023) PUBLISHER_CC http://dx.doi.org/10.3390/buildings13071793 Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Tanasra, Hanan Rott Shaham, Tamar Michaeli, Tomer Austern, Guy Barath, Shany Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts |
| title | Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts |
| title_full | Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts |
| title_fullStr | Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts |
| title_full_unstemmed | Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts |
| title_short | Automation in Interior Space Planning: Utilizing Conditional Generative Adversarial Network Models to Create Furniture Layouts |
| title_sort | automation in interior space planning utilizing conditional generative adversarial network models to create furniture layouts |
| url | https://hdl.handle.net/1721.1/151180 |
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