Purpose:
This study aimed to evaluate the accuracy, consistency, and clinical relevance of four 3D facial scanning technologies—photogrammetry, AI-assisted capture, and structured light devices—for digital facial reconstruction in prosthetic dentistry. The goal was to validate accessible digital workflows by comparing digital measurements to conventional anthropometric methods and to identify the most efficient solutions for clinical integration.

Methods:
A clinical cohort of thirty adult participants requiring facial prosthetic rehabilitation underwent 3D facial scanning using four technologies: Polycam (photogrammetry), Luma AI (AI-assisted), Bellus3D (structured light), and Revopoint POP 2 (light-based scanning). Traditional anthropometric measurements were used as reference. All 3D models were scaled and standardized in Blender using a custom Python script. Anatomical landmarks were manually positioned, and Euclidean distances were calculated. Statistical analysis included ANOVA, ICC, Pearson correlation, and Bland–Altman plots.

Results:
Revopoint exhibited the highest accuracy (0.12 ± 0.03 mm) and consistency (ICC = 0.98), followed by Polycam (0.15 ± 0.04 mm; ICC = 0.96). Notably, Polycam achieved the shortest scan time (18 ± 2 s), offering a rapid and patient-friendly option. Luma AI and Bellus3D performed lower across all metrics. Statistically significant differences (p < 0.001) were observed among devices. Revopoint and Polycam showed the closest agreement with reference data.

Conclusions:
While Revopoint remains the gold standard in precision, Polycam emerges as a clinically viable, time-efficient, and accessible tool for facial scanning. The proposed workflow is reproducible and adaptable for integration into digital prosthetic workflows. Further research should explore automated landmark detection and clinical validation on larger cohorts.