| id |
cdrf2021_26 |
| authors |
Yuqian Li and Weiguo Xu |
| year |
2021 |
| title |
Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-16-5983-6_3
|
| source |
Proceedings of the 2021 DigitalFUTURES
The 3rd International Conference on Computational Design and Robotic Fabrication (CDRF 2021) |
| summary |
Architects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial
Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process.By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology. |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (2,085,859 bytes) |
| references |
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| last changed |
2022/09/29 07:53 |
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