Applying Ming furniture features to modern furniture design using deep learning

Abstract: Ming-style furniture is a type of classical Chinese furniture that originated during the Ming Dynasty and has developed and evolved through the Ming and Qing Dynasties to become one of the major schools of classical Chinese furniture. Traditional Ming furniture design is labour-intensive and time-consuming, and designers need to have a wealth of experience and knowledge to create high-quality pieces. However, with the rapid development of computer technology and advances in deep learning algorithms, it is now possible to use computer-aided design techniques and deep learning algorithms to extract and apply features of Ming furniture quickly and accurately.This paper proposes a new method based on deep learning and computer-aided design techniques for applying features of Ming furniture to modern furniture design. By collecting and filtering existing physical images of Ming-style furniture, we use a generative adversarial network algorithm (DCGAN) for image recognition and feature extraction, and generate modern furniture designs. The experimental results show that the algorithm can significantly improve the efficiency of designers and has good feature recognition to extract target contours and accurately obtain design features. As the number of extracted feature samples increases, the clarity of the generated images becomes higher and their generation accuracy also tends to increase. The furniture products generated by the deep learning approach have both modern aesthetics and Ming furniture characteristics, which is conducive to the inheritance and development of traditional Chinese furniture culture. The evaluation shows that the newly generated furniture products meet modern aesthetic standards. The method provides a new way of thinking and approach to the field of furniture design, with high academic and practical application value.

Keywords: Deep learning, Ming furniture, Product design, Image recognition, Computer-aided design

DOI: 10.54941/ahfe1004197

Cite this paper:

​