Scholarlink

<p>Semantic segmentation of Buddha facial point clouds guided by canonical proportional rules enables segmentation without manual labels while ensuring semantic consistency and structural continuity. Existing data-driven methods require large annotated datasets, often unavailable for Buddhist statue faces, while classical model-driven methods capture geometry but lack semantic consistency with sculptural canons. This study presents a knowledge-guided method that embeds bilateral symmetry, proportional divisions, and canonical anchors into a grid-based region-growing process with adaptive convexity thresholds, followed by morphological refinement to maintain continuity and reduce fragmentation. The method achieves F1-scores above 0.85 and mean IoU around 0.80 across diverse Buddha statue samples. These results indicate that canonical sculptural principles can be identified through computational analysis.</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

Semantic segmentation of Buddha facial point clouds through knowledge-guided region growing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月18日

查看全文
  • Songze Wei,
  • Miaole Hou,
  • Su Yang,
  • Huabiao Li,
  • Songnian Li

摘要

Semantic segmentation of Buddha facial point clouds guided by canonical proportional rules enables segmentation without manual labels while ensuring semantic consistency and structural continuity. Existing data-driven methods require large annotated datasets, often unavailable for Buddhist statue faces, while classical model-driven methods capture geometry but lack semantic consistency with sculptural canons. This study presents a knowledge-guided method that embeds bilateral symmetry, proportional divisions, and canonical anchors into a grid-based region-growing process with adaptive convexity thresholds, followed by morphological refinement to maintain continuity and reduce fragmentation. The method achieves F1-scores above 0.85 and mean IoU around 0.80 across diverse Buddha statue samples. These results indicate that canonical sculptural principles can be identified through computational analysis.