College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, 29 Yudao Street, Qinhuai Dist, Nanjing, 210016, People's Republic of China.
Research Centre of Engineering and Technology for Computerized Dentistry, Ministry of Health, Peking University School and Hospital of Stomatology, Beijing, 100081, People's Republic of China.
Med Biol Eng Comput. 2017 Sep;55(9):1635-1647. doi: 10.1007/s11517-017-1626-x. Epub 2017 Feb 7.
With the development of 3D printing and computer graphics technology, mouth rehabilitation has increasingly adopted digital methods. This research proposes a new method to transform the appearance of facial model after complete denture prosthesis. A feature template with few feature points is first constructed according to the facial muscle anatomy and facial deformation after complete denture prosthesis. Next, the traditional as-rigid-as-possible (ARAP) method is optimised by clustering based on facial muscles. The optimised ARAP method is then used for real-time and interactive simulations. Finally, by classifying the degrees of elasticity in the model with additional weights, the simulation can be customised to the skin of individual patients. Different degrees of elastic deformation and post-operative models are superimposed for match analysis. Compared with our previous study, the error is reduced by 24.05%. Results show that our method can deform facial models accurately and efficiently.
随着 3D 打印和计算机图形技术的发展，口腔修复越来越多地采用数字化方法。本研究提出了一种新的方法，用于改变全口义齿修复后面部模型的外观。首先，根据面部肌肉解剖结构和全口义齿修复后面部变形，构建具有少量特征点的特征模板。接下来，基于面部肌肉对传统的尽可能刚性（ARAP）方法进行聚类优化。然后使用优化的 ARAP 方法进行实时和交互式模拟。最后，通过对模型的弹性程度进行分类并添加额外的权重，可根据个体患者的皮肤对模拟进行定制。对不同程度的弹性变形和术后模型进行叠加，进行匹配分析。与我们之前的研究相比，误差降低了 24.05%。结果表明，我们的方法可以准确有效地对面部模型进行变形。
