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

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.