| id |
cdrf2022_314 |
| authors |
Yuqian Li, Weiguo Xu, and Xingchen Liu |
| year |
2022 |
| title |
Research on Architectural Generation Design of Specific Architect's Sketch Based on Image-To-Image Translation |
| 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_28
|
| summary |
Sketch is a way for architects to communicate with others. Architects record their own ideas through rapid drawing. However, sketches are abstract, vague, and even ambiguous. To this end, architects need to spend a lot of time, through modeling and other means, to present the architectural plan that can be understood by people. However, this method is time-consuming and laborious. Due to the development of deep learning technology, especially convolutional neural networks (CNN) and generative adversarial networks (GAN), they have shown great advantages in the field of image recognition and generation. With the help of these technologies, ambiguous architectural sketches can be directly transformed into architectural scheme drawings, and architects’ creative intentions can be continuously improved and developed, It will be very convenient and efficient. Therefore, based on the image-to-image translation, this paper realizes the mapping from architectural sketches to architectural scheme drawings with the help of CycleGAN. Through the analysis of the architectural generation design results of Frank Gehry's and Alberto Campo Baeza's architectural sketches, firstly, the feasibility of this method is verified. Secondly, it is found that this method can well complete the identification of sketch boundaries. In the generated scheme drawings, it can not only reflect the volume and lighting changes of the building, but also reflect the architect's creative intention and style to a large extent, The side reflects the cognitive ability of this method to architectural design. |
| series |
cdrf |
| email |
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| full text |
 file.pdf (723,101 bytes) |
| references |
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| last changed |
2024/05/29 14:02 |
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