| id |
cdrf2022_359 |
| authors |
Qiaoming Deng, Xiaofeng Li, and Yubo Liu |
| year |
2022 |
| title |
Using Pix2Pix to Achieve the Spatial Refinement and Transformation of Taihu Stone |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_31
|
| source |
Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022) |
| summary |
Under the impact of globalization, the transformation of traditional architectural space is particularly important for the development of local architecture. As an important spatial component of traditional gardens, Taihu stone has the image characteristics of “thin, wrinkled, leaky and transparent”. The “transparency” and “ leaky” of Taihu stone reflect the connectivity and irregularity of the holes of Taihu stone, which are in line with the ideas of flowing space and transparency in contemporary architectural design. However, there are relatively few theoretical studies on the spatial analysis and design transformation of Taihu stone. The Pix2Pix model extracts the 3D spatial variation pattern by learning the variation pattern between two adjacent slices of Taihu stone. The trained Pix2Pix model can generate a series of continuous spatial sections with the spatial variation pattern of Taihu stone. Finally, the 2D sections are transformed into 3D building volumes to complete the spatial translation of Taihu stone in contemporary architectural design. In addition, this paper also provides a new idea for machine learning to master the continuous 3D spatial change pattern. |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (688,027 bytes) |
| references |
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| last changed |
2024/05/29 14:03 |
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