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<p>Buddhist sculptures face preservation challenges due to physical degradation and limited accessibility. We present a framework for high-fidelity 3D reconstruction from single 2D images, integrating modified Stable Diffusion architecture with a novel Instant and Consistent Mesh Reconstruction (ISOMER) algorithm. Our method employs multi-stage processing: multi-view image generation, high-resolution refinement via ControlNet and Real-ESRGAN, and normal map prediction. Enhanced by a curated dataset of 672 Buddhist sculptures, we introduce Explicit Target optimization in ISOMER, significantly improving fine iconographic detail preservation. Experimental results demonstrate superior performance over state-of-the-art methods, achieving 7.1% reduction in Chamfer Distance and 2.2% increase in F-Score. The framework captures intricate surface textures and stylistic elements crucial for art historical research and digital conservation, though limitations persist in reconstructing highly reflective or translucent materials.</p>

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High-fidelity 3D Buddhist sculpture reconstruction from single images using domain-adaptive diffusion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月18日

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  • Shunfeng Zhang,
  • Weifan Wang,
  • Bingjie Lu,
  • Zhengyang Lu

摘要

Buddhist sculptures face preservation challenges due to physical degradation and limited accessibility. We present a framework for high-fidelity 3D reconstruction from single 2D images, integrating modified Stable Diffusion architecture with a novel Instant and Consistent Mesh Reconstruction (ISOMER) algorithm. Our method employs multi-stage processing: multi-view image generation, high-resolution refinement via ControlNet and Real-ESRGAN, and normal map prediction. Enhanced by a curated dataset of 672 Buddhist sculptures, we introduce Explicit Target optimization in ISOMER, significantly improving fine iconographic detail preservation. Experimental results demonstrate superior performance over state-of-the-art methods, achieving 7.1% reduction in Chamfer Distance and 2.2% increase in F-Score. The framework captures intricate surface textures and stylistic elements crucial for art historical research and digital conservation, though limitations persist in reconstructing highly reflective or translucent materials.