Restore Traditional Chinese Lanterns Based on Openscad and Threejs
- DOI
- 10.2991/978-94-6463-370-2_53How to use a DOI?
- Keywords
- Modelling; Traditional Culture; Practical; Chinese Traditional Lantern
- Abstract
With the continuous development of modern computer graphics technology, its scope of application is becoming increasingly broad. This also has a positive impact on the preservation of traditional culture, as modern modeling techniques can digitally recreate many traditional buildings and objects. This article aims to combine OpenSCAD and Three.js to reproduce traditional Chinese lanterns in a minimalist style and explore the integration of traditional culture and modern technology. Then build the website with Vue3(The Progressive JavaScript Framwork) + Vite + ElementPlus + Threejs to display the final result model, detailed code and model report. Finally, a random selection of interviewees was conducted to assess the overall model restoration, the compatibility of this style with traditional Chinese lanterns, and the quality of the scene rendering. The results indicate that the evaluation of the model quality and scene rendering is generally positive. However, there were some negative assessments regarding the level of restoration and scene quality, suggesting the need for further optimization and improvements.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Linghan Zheng PY - 2024 DA - 2024/02/14 TI - Restore Traditional Chinese Lanterns Based on Openscad and Threejs BT - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023) PB - Atlantis Press SP - 526 EP - 537 SN - 1951-6851 UR - https://doi.org/10.2991/978-94-6463-370-2_53 DO - 10.2991/978-94-6463-370-2_53 ID - Zheng2024 ER -