| id |
cdrf2019_17 |
| authors |
Chuan Liu, Jiaqi Shen, Yue Ren, and Hao Zheng |
| year |
2020 |
| title |
Pipes of AI – Machine Learning Assisted 3D Modeling Design |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_2
|
| source |
Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020) |
| summary |
Style transfer is a design technique that is based on Artificial Intelligence and Machine Learning, which is an innovative way to generate new images with the intervention of style images. The output image will carry the characteristic of style image and maintain the content of the input image. However, the design technique is employed in generating 2D images, which has a limited range in practical use. Thus, the goal of the project is to utilize style transfer as a toolset for architectural design and find out the possibility for a 3D modeling design. To implement style transfer into the research, floor plans of different heights are selected from a given design boundary and set as the content images, while a framework of a truss structure is set as the style image. Transferred images are obtained after processing the style transfer neural network, then the geometric images are translated into floor plans for new structure design. After the selection of the tilt angle and the degree of density, vertical components that connecting two adjacent layers are generated to be the pillars of the structure. At this stage, 2D style transferred images are successfully transformed into 3D geometries, which can be applied to the architectural design processes. Generally speaking, style transfer is an intelligent design tool that provides architects with a variety of choices of idea-generating. It has the potential to inspire architects at an early stage of design with not only 2D but also 3D format. |
| series |
cdrf |
| email |
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| full text |
 file.pdf (3,855,154 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/09/29 07:51 |
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