Geometric Deep Learning for Shape Correspondence in Mass Customization by Three-Dimensional Printing
Geometric Deep Learning for Shape Correspondence in Mass Customization by Three-Dimensional Printing
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DOI：
10.1115/1.4046746
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发表时间：
2020-06-01
影响因子：
4
通讯作者：
Xu, Wenyao
中科院分区：
文献类型：
--
作者：
Huang, Jida;Sun, Hongyue;Xu, Wenyao
许多行业，如以人为中心的产品制造业，正在呼吁大规模定制个性化产品。大规模定制的一个关键推动因素是3D打印，它使灵活的设计和制造成为可能。然而，个性化设计的高度复杂性和形状变化给形状匹配和分析带来了挑战。传统的形状匹配方法局限于空间对齐和寻找两个形状的变换矩阵，这不能确定两个形状之间的顶点到顶点或特征到特征的相关性。因此，这种方法不能直接测量形状和感兴趣的特征的变形。为了测量大规模定制模式中广泛存在的变形，并解决形状匹配中的对齐方法问题，我们将变形形状的几何匹配确定为对应问题。由于该问题的解空间巨大且具有非线性的复杂性，传统的优化方法难以解决，因此具有挑战性。针对现有的海量数据库提供的牙齿形状对应结果，提出了一种基于学习的牙齿形状对应方法。具体地，使用最先进的几何深度学习方法来学习一组收集的变形形状的对应关系。通过学习模型的变形，提取形状的潜在变化，并用于找到这些形状之间的顶点到顶点的映射。实验结果表明，该方法能够快速准确地预测对应关系，对极端情况具有较强的鲁棒性。此外，所提出的方法是有利的，适用于变形形状分析的大规模定制3D打印。
Many industries, such as human-centric product manufacturing, are calling for mass customization with personalized products. One key enabler of mass customization is 3D printing, which makes flexible design and manufacturing possible. However, the personalized designs bring challenges for the shape matching and analysis, owing to the high complexity and shape variations. Traditional shape matching methods are limited to spatial alignment and finding a transformation matrix for two shapes, which cannot determine a vertex-to-vertex or feature-to-feature correlation between the two shapes. Hence, such a method cannot measure the deformation of the shape and interested features directly. To measure the deformations widely seen in the mass customization paradigm and address the issues of alignment methods in shape matching, we identify the geometry matching of deformed shapes as a correspondence problem. The problem is challenging due to the huge solution space and nonlinear complexity, which is difficult for conventional optimization methods to solve. According to the observation that the well-established massive databases provide the correspondence results of the treated teeth models, a learning-based method is proposed for the shape correspondence problem. Specifically, a state-of-the-art geometric deep learning method is used to learn the correspondence of a set of collected deformed shapes. Through learning the deformations of the models, the underlying variations of the shapes are extracted and used for finding the vertex-to-vertex mapping among these shapes. We demonstrate the application of the proposed approach in the orthodontics industry, and the experimental results show that the proposed method can predict correspondence fast and accurate, also robust to extreme cases. Furthermore, the proposed method is favorably suitable for deformed shape analysis in mass customization enabled by 3D printing.