用形态测量法评估手机与AI重建人脸的差异
A 3D Facial Reconstruction Evaluation Methodology: Comparing Smartphone Scans with Deep Learning Based Methods Using Geometry and Morphometry Criteria
- 融合几何与形态测量学构建新评估框架
- 以高端立体摄影为基准，量化形变差异
- 适合临床用低成本人脸采集技术验证
三维人脸形状分析因潜在临床应用而受到关注。然而，先进3D人脸获取系统的高成本限制了其广泛应用，推动了低成本获取与重建方法的发展。本研究提出一种新型评估方法，超越传统几何基准，引入形态测量学分析技术，建立统计框架以评估面部形态的保持情况。以智能手机3D扫描与基于深度学习的2D图像重建方法为例，使用高端立体摄影模型作为真实基准进行对比。该方法可定量评估全局与局部形状差异，为低成本3D人脸获取与重建技术提供具有生物学意义的验证途径。
原文摘要 · Abstract (English)
Three-dimensional (3D) facial shape analysis has gained interest due to its potential clinical applications. However, the high cost of advanced 3D facial acquisition systems limits their widespread use, driving the development of low-cost acquisition and reconstruction methods. This study introduces a novel evaluation methodology that goes beyond traditional geometry-based benchmarks by integrating morphometric shape analysis techniques, providing a statistical framework for assessing facial morphology preservation. As a case study, we compare smartphone-based 3D scans with state-of-the-art deep learning reconstruction methods from 2D images, using high-end stereophotogrammetry models as ground truth. This methodology enables a quantitative assessment of global and local shape differences, offering a biologically meaningful validation approach for low-cost 3D facial acquisition and reconstruction techniques.
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