Abstract: To address the insufficient perception of geometric pose relationships and the difficulty of unified modeling for multiple tooth types in existing three dimensional dental keypoint detection methods, a quaternion based geometric aware and adaptive expert network (QGAE Net) is proposed. The method introduces a Multi Scale Quaternion based Geometric Positional Encoder (MS QGPE) that combines quaternion representation with geometric shape descriptors to learn local to global geometric structures of point clouds and enhance spatial relationship modeling. A Quaternion Guided Geometric Pose Attention (QG GPA) module is designed to constrain attention weights using quaternion similarity, allowing feature aggregation according to true geometric correlations. Furthermore, a Classification Driven Expert Routing Mechanism (CD ERM) is constructed to achieve unified modeling of heterogeneous tooth types and personalized feature learning through dynamically activated expert subnetworks. Experiments conducted on a clinical dataset containing 19,200 tooth samples demonstrate that the proposed method achieves mean absolute errors of 0.179 mm, 0.233 mm, 0.188 mm, and 0.301 mm for incisors, canines, premolars, and molars, respectively, with corresponding recalls of 85.1%, 87.1%, 91.5%, and 67.5%, and an overall classification accuracy of 97.5%. In addition, experiments on the public Teeth3DS+ and KeypointNet datasets demonstrate consistent performance improvements over existing methods, confirming the model’s strong generalization capability on public benchmarks and cross-category scenarios. Overall, QGAE Net effectively enhances keypoint detection accuracy while maintaining high deployment efficiency and scalability, making it suitable for automatic landmark annotation across diverse dental scenarios.

摘要： 为解决现有三维牙齿关键点检测方法中存在的几何姿态感知不足及多类型牙齿统一建模困难问题，提出了一种基于四元数几何感知与自适应专家网络（QGAE-Net）的关键点检测算法。该方法设计了多尺度几何位置编码器（MS-QGPE），利用四元数表示结合几何形状描述子对点云的局部与全局结构进行多尺度建模，增强几何关系感知能力；构建了四元数引导的几何姿态注意力模块（QG-GPA），通过引入四元数相似度对注意力权重施加约束，引导模型依据真实几何关系完成特征聚合；同时，引入分类驱动的专家路由机制（CD-ERM），通过动态激活不同专家子网络实现多牙型的统一建模与个性化特征提取。在包含19200个牙齿样本的临床数据集上，所提方法在切牙、尖牙、前磨牙和磨牙上的平均绝对误差分别为0.179 mm、0.233 mm、0.188 mm和0.301 mm，召回率分别为85.1%、87.1%、91.5%和67.5%，整体分类准确率达到97.5%。此外，在Teeth3DS+与KeypointNet公开数据集上亦获得优于现有方法的关键点检测性能，验证了模型在公开基准与跨类别场景下的泛化能力。综合结果显示，QGAE-Net不仅有效提升了关键点检测精度，也具备较高的部署效率和结构扩展性，适用于多类复杂牙型场景下的自动化关键点标注任务。