| id |
cdrf2019_159 |
| authors |
Hang Zhang and Ye Huang |
| year |
2020 |
| title |
Machine Learning Aided 2D-3D Architectural Form Finding at High Resolution |
| source |
Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020) |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_15
|
| summary |
In the past few years, more architects and engineers start thinking about the application of machine learning algorithms in the architectural design field such as building facades generation or floor plans generation, etc. However, due to the relatively slow development of 3D machine learning algorithms, 3D architecture form exploration through machine learning is still a difficult issue for architects. As a result, most of these applications are confined to the level of 2D. Based on the state-of-the-art 2D image generation algorithm, also the method of spatial sequence rules, this article proposes a brand-new strategy of encoding, decoding, and form generation between 2D drawings and 3D models, which we name 2D-3D Form Encoding WorkFlow. This method could provide some innovative design possibilities that generate the latent 3D forms between several different architectural styles. Benefited from the 2D network advantages and the image amplification network nested outside the benchmark network, we have significantly expanded the resolution of training results when compared with the existing form-finding algorithm and related achievements in recent years |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (6,869,384 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/09/29 07:51 |
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