| id |
cdrf2022_233 |
| authors |
Yubo Liu, Zhilan Zhang, and Qiaoming Deng |
| year |
2022 |
| title |
Exploration on Diversity Generation of Campus
Layout Based on GAN |
| source |
Proceedings of the 2022 DigitalFUTURES The 4st International Conference on Computational Design and Robotic Fabrication (CDRF 2022) |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-19-8637-6_20
|
| summary |
Previous studies have shown that GAN has made some progress in the generation of campus layout plan, but the result is single output for single input condition. This paper hopes to make some attempts and explorations on the diversity generation of campus planning layout design by machine learning. Based on Pix2Pix model, this paper proposes a method to divide image channels so that both the campus function bubble diagram and the site boundary can both become the input conditions. There is a strong correspondence between the campus functional bubble diagram and the campus layout. The main idea of this study is to control the generated results by changing the input of the campus functional bubble diagram, so that we can have a diversity layout of campus according to the same site conditions. In the experiment, we train thirty samples of campus planning layout design, and finally evaluate the generated results in a qualitative and quantitative way, which proves that the generated results are relatively ideal. This research enables designers to participate in the process of machine learning generative design to control the generation results. |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (866,888 bytes) |
| references |
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