Automatic Tooth Alignment Method Based on Mesh Features

doi:10.3969/j.issn.1004-132X.2025.11.032

Abstract

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 decoder and 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 loss function， which helped to prevent overfitting 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

摘要：

针对基于点云的深度学习自动排牙方法数据依赖性强、咬合准确度低等问题，提出了一种基于网格特征的深度学习自动排牙方法。设计的模型包括形状编码器、全局特征编码器、特征解码与映射器以及牙齿咬合生成网络。形状编码器从牙齿模型表面的三角网格数据中提取牙齿形状特征，全局特征编码器从简化后的牙齿点云中提取牙列全局特征，特征解码与映射器则对牙齿全局特征、牙齿局部特征进行融合降维，生成最终的排牙结果，减少了数据依赖性，牙齿咬合生成网络基于颌骨空间位置关系和牙齿特征生成上下牙咬合面，提高了上下牙咬合准确性。为进一步提高模型性能，在损失函数中引入了相似性损失函数，有助于防止过拟合，提高了自动排牙的质量。实验结果表明，与四种现有方法相比，该方法在ADD指标上均有降低，显著提高了深度学习自动排牙的准确度。

关键词: 自动排牙, 网格特征, 形状编码器, 牙齿咬合生成网络

CLC Number:

TP391

Mengjie HU, Yuhang FANG, Xujia QIN, Zhengqiang WU. Automatic Tooth Alignment Method Based on Mesh Features[J]. China Mechanical Engineering, 2025, 36(11): 2738-2746.

胡梦杰, 方宇航, 秦绪佳, 吴正强. 基于网格特征的自动排牙方法[J]. 中国机械工程, 2025, 36(11): 2738-2746.

Figures/Tables 11

Fig.1 Structure of Mbtanet model

Fig.2 Shape encoder

Fig.3 Tooth occlusion generating network

Fig.4 Results of enhance model

Fig.5 Comparison of precision under different methods

Tab.1 Performance comparison of different methods

方法	ADD	PA-ADD	CSA	MErot	FDcur	Mean TRE
PSTN	1.793	1.447	0.785	6.103	2.565	0.3426
TAligNet	1.761	1.425	0.789	5.922	2.678	0.3354
TANet	1.597	1.333	0.823	5.585	2.321	0.3201
TADPM	1.582	1.316	0.859	5.581	2.229	0.3125
Mbtanet（本文）	1.562	1.299	0.878	5.585	2.207	0.3001

Fig.6 Comparison of dental alignment results

Fig.7 Results of different occlusion problems

Tab.2 Results of ablation experiment

损失	ADD	PA-ADD	CSA	MErot	FDcur	Mean TRE
$L C D$	1.648	1.456	0.849	5.658	2.378	0.3194
$L p o s$	1.635	1.359	0.834	5.669	2.285	0.3207
$L d o f$	1.728	1.556	0.836	5.681	2.364	0.3266
$L C D$ + $L p o s$ + $L d o f$	1.571	1.347	0.852	5.640	2.247	0.3139
$L$	1.562	1.299	0.878	5.585	2.207	0.3001

Tab.2 Results of ablation experiment

损失	ADD	PA-ADD	CSA	MErot	FDcur	Mean TRE
$L C D$	1.648	1.456	0.849	5.658	2.378	0.3194
$L p o s$	1.635	1.359	0.834	5.669	2.285	0.3207
$L d o f$	1.728	1.556	0.836	5.681	2.364	0.3266
$L C D$ + $L p o s$ + $L d o f$	1.571	1.347	0.852	5.640	2.247	0.3139
$L$	1.562	1.299	0.878	5.585	2.207	0.3001

Fig.8 Test result of dental alignment example

Fig.9 Accuracy of tooth arrangement testing

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