目的 针对头面部相关穿戴产品定制过程中,过度依赖三维扫描仪获取3D头面部数据的局限性,研究构建一种基于2D尺寸预测3D头面部形状的算法。方法 首先,在确保重建误差小的基础上,分别采用非刚性迭代最近点算法和广义普洛克算法对大规模采集的3D头面部模型进行参数化和对齐处理。随后,基于主成分分析的方法构建3D头面部统计形状模型,并通过紧致性、泛化性及特异性验证其稳定性。以统计形状模型为数据基础,结合2D测量尺寸,通过构建的前向与后向搜索算法,计算2D尺寸到3D头面部模型的回归矩阵,以利用较少维度的2D尺寸组合实现更高精度的3D头面部形状预测。结果 预测结果表明,最优2D测量尺寸数量为7,平均预测误差为(2.97±0.61) mm。最后,以口罩定制设计为例,通过应力仿真分析及密合性分析,验证了预测模型的可靠性。结论 该研究成果可拓展至人体其他部位的3D预测,从而为头面部以外的在线个性化定制提供新的解决方案。
Abstract
To address the limitations of relying excessively on 3D scanners for acquiring head and facial data in the customization of wearable products, the work aims to propose and develop an algorithm for predicting 3D head and facial shapes based on 2D measurements. Firstly, under the premise of minimizing reconstruction errors, large-scale 3D head and facial models were parameterized and aligned with a non-rigid iterative closest point algorithm and a generalized Procrustes algorithm, respectively. Subsequently, statistical shape models of the 3D head and face were constructed through principal component analysis, and their stability was validated through compactness, generalization, and specificity tests. Building on the SSM, forward and backward search algorithms were established to compute regression matrices between 2D measurements and 3D models, enabling accurate 3D head and facial predictions with a reduced set of 2D dimensions. Prediction results indicated that the optimal number of 2D measurements was seven, achieving an average prediction error of 2.97 (±0.61) mm. Finally, with mask customization as a case study, the reliability of the prediction model was verified through stress simulation and airtightness analysis. It is worth mentioning that the findings of this study can be extended to the 3D prediction of other body parts, providing new solutions for online personalization beyond the head and face.
关键词
3D头面部模型 /
非刚性配准 /
统计形状模型 /
搜索算法 /
定制设计
Key words
3D head and facial model /
non-rigid iterative closest point /
statistical shape model /
search algorithm /
customized design
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基金
中央高校基本科研业务经费专项资金资助（2026QNSK46）; 国家自然科学基金（52005498）
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