HTLNet: A Segmentation-Free Multi-View Approach for Robust 3D Tooth Landmark Localization

Tooth landmark localization plays a pivotal role in digital orthodontics, providing the computational foundation for generating alignment coordinates and guiding precise treatment planning. However, the limited availability of high-quality 3D tooth landmark datasets and the prevalent reliance on seg-mentation-based methods hinder the accuracy and scalability of current ap-proaches. In this work, we manually annotated a publicly available dental dataset to construct a benchmark 3D tooth landmark dataset, which provides a foundation for robust evaluation in real-world clinical scenarios. To over-come the limitations of existing methods, we propose HTLNet Heatmap-based Tooth Landmark Localization Network , a novel segmentation-free lo-calization framework based on multi-view 2D heatmap regression. HTLNet eliminates the dependency on prior segmentation and reduces error propaga-tion in the processing pipeline. Experimental results demonstrate that HTLNet outperforms state-of-the-art 3D models, such as PointNet-Reg, in terms of accuracy and robustness, especially under challenging conditions such as missing teeth or misaligned dentition. Our method provides a gener-alizable, scalable, and efficient solution, making it well-suited for integration into intelligent dental digital systems and advancing the application of com-puter vision technologies in digital healthcare.