基于网格特征的自动排牙方法

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中图分类号：TP391 DOI：10.3969/j.issn.1004-132X.2025.11.032

Abstract：Aiming at the problems of strong data dependence and low occlusion accuracy of point cloud-based deep learning automatic tooth alignment method，a deep learning automatic tooth alignment method was proposed based on mesh features. The design model included shape encoder，global feature encoder，feature decoder and mapper，and tooth occlusion generation network. The shape encoder extracted the tooth shape features from the triangular mesh data on the surfaces of the tooth model，the global feature encoder extracted the global features of the tooth set from the simplified tooth point clouds，and the feature decoderand mapper fused and reduced the dimension of the global features and local features of the tooth to generate the final tooth arrangement results，reducing the data dependence. The tooth occlusion network generated the upper and lower occlusal surfaces based on the spatial position of the jaw and the characteristics of the teeth，which improved the accuracy of the upper and lower occlusal surfaces.In order to further improve the performance of the model，the similarity loss function was introduced into the los function， which helped to prevent overfiting and improve the quality of automatic tooth alignment.The experimental results show that compared with four existing methods，the proposed method reduces the ADD index，and significantly improves the accuracy of deep learning automatic tooth alignment.

Key words： automatic tooth alignment; mesh feature; shape encoder;tooth occlusion generating network

0引言

排牙是正畸治疗[-2]的关键步骤，可以提高患者的整体健康水平[3]，增强面部美观度[4-5]。（剩余13571字）