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Constructing dense correspondences for the analysis of 3D facial morphology

机译:构建密集的对应关系以分析3D面部形态

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In this paper, we present a method for constructing dense correspondences between 3D open surfaces that is sufficiently accurate to permit clinical analysis of 3D facial morphology. Constructing dense correspondences between 3D models representing facial surface anatomy is a natural extension of landmark-based methods for analysing facial shape or shape changes. Compared to landmark-based methods, dense correspondences sample the entire surface and hence provide a more thorough description of the underlying 3D structures. The method we present here is based on elastic deformation, which deforms a 3D generic model onto the 3D surface of a specific individual. We are then able to construct dense correspondences between different individuals by analysing their corresponding deformed generic models. Validation experiments show that, using only five manually placed landmarks, approximately 95% of triangles on the deformed generic mesh model are within the range of ± 0.5 mm to the corresponding original model. The established dense correspondences have been exploited within a principal components analysis (PCA)-based procedure for comparing the facial morphology of a control group to that of a surgically managed group comprising the patients who have been subject to facial lip repair.
机译:在本文中,我们提出了一种在3D开放表面之间构建密集对应关系的方法,该方法足够准确,可以对3D面部形态进行临床分析。在表示面部表面解剖结构的3D模型之间构造密集的对应关系是基于地标的方法的自然扩展,用于分析面部形状或形状变化。与基于地标的方法相比,密集的对应对整个表面进行采样,因此提供了对底层3D结构的更彻底的描述。我们在此介绍的方法基于弹性变形,该变形将3D通用模型变形到特定个体的3D表面上。然后,我们可以通过分析不同个体的变形通用模型来构造不同个体之间的密集对应关系。验证实验表明,仅使用五个手动放置的界标,变形的通用网格模型上大约95%的三角形在相应原始模型的±0.5 mm范围内。已建立的密集对应关系已在基于主成分分析(PCA)的程序中得到利用,用于将对照组的面部形态与包括接受了唇部修复的患者的手术治疗组的面部形态进行比较。

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