Scholarlink

<p>Traditional Chinese painting is an essential component of cultural heritage, and its 3D digitalization plays a crucial role in cultural dissemination and artistic reproduction. However, existing Non-Photorealistic Rendering (NPR) approaches face limitations in reproducing Chinese paintings, such as loss of artistic spirit, unclear workflows, and high learning costs for creators. To address these issues, this study introduces a “Chinese Painting Technique–NPR Algorithm Matching Matrix,” which quantitatively maps five major categories of traditional techniques to 24 mainstream NPR algorithms, providing systematic guidelines for algorithm selection. Based on this framework, we develop a layered collaborative 3D digitalization pipeline, exemplified through Qian Xuan’s <i>Eight Flowers</i>, and incorporate an Artist-in-the-loop mechanism to ensure artistic fidelity. Furthermore, a multidimensional evaluation system was established, combining general metrics (LPIPS, SSIM) with newly designed indicators for color fidelity, brushstroke texture, surface texture, and glossiness, alongside subjective evaluation via blind tests. The results demonstrate that our method achieves a favorable balance between artistic authenticity and computational efficiency, outperforming existing projects in both objective scores and perceptual realism. This research not only provides a new pathway for the digital preservation and dissemination of Chinese paintings but also extends the application potential of NPR techniques in cultural heritage.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Non-photorealistic rendering for 3D digitalization of Chinese painting based on a technique–algorithm matrix with a case study of Qian Xuan’s Eight Flowers

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月24日

查看全文
  • Meiru Yin,
  • Wenzhuo Zhao

摘要

Traditional Chinese painting is an essential component of cultural heritage, and its 3D digitalization plays a crucial role in cultural dissemination and artistic reproduction. However, existing Non-Photorealistic Rendering (NPR) approaches face limitations in reproducing Chinese paintings, such as loss of artistic spirit, unclear workflows, and high learning costs for creators. To address these issues, this study introduces a “Chinese Painting Technique–NPR Algorithm Matching Matrix,” which quantitatively maps five major categories of traditional techniques to 24 mainstream NPR algorithms, providing systematic guidelines for algorithm selection. Based on this framework, we develop a layered collaborative 3D digitalization pipeline, exemplified through Qian Xuan’s Eight Flowers, and incorporate an Artist-in-the-loop mechanism to ensure artistic fidelity. Furthermore, a multidimensional evaluation system was established, combining general metrics (LPIPS, SSIM) with newly designed indicators for color fidelity, brushstroke texture, surface texture, and glossiness, alongside subjective evaluation via blind tests. The results demonstrate that our method achieves a favorable balance between artistic authenticity and computational efficiency, outperforming existing projects in both objective scores and perceptual realism. This research not only provides a new pathway for the digital preservation and dissemination of Chinese paintings but also extends the application potential of NPR techniques in cultural heritage.