DGLL: A Hybrid Global-Local Feature Learning Network for Precise Tooth Landmark Detection

The precise identification of key landmarks on three-dimensional tooth mesh models is paramount for computer-aided orthodontic treatment. However, existing methodologies exhibit limitations with respect to the integration of global and local features, which undermines accuracy in complex scenarios and excessively emphasizes relative landmark positions, resulting in displacement errors. To mitigate these issues, this study introduces DGLL, a hybrid feature learning network characterized by a dual-branch architecture that amalgamates global and local features. DGLL integrates a Cascaded Topological Relation Module (CTRM) to stabilize the extraction of global features and a Pan-scale Feature Modulation Module (PFMM) to balance relative and absolute positional accuracy. Empirical evaluations across various tooth types demonstrate that DGLL consistently enhances the accuracy of landmark localization. This research provides an effective approach to the automated analysis of tooth data, thereby improving the precision and efficacy of orthodontic treatment.