CHaRNet: Conditioned Heatmap Regression for Robust Dental Landmark Localization

Identifying anatomical landmarks in 3D dental models is vital for orthodontic treatment, yet manual placement is complex and time-consuming. Although some machine learning approaches have been proposed for automatic tooth landmark detection in 3D Intraoral Scans (IOS), none provide a fully end-to-end solution that bypasses teeth segmentation, limiting practical applicability. We introduce CHaRNet (Conditioned Heatmap Regression Network), the first fully end-to-end deep learning framework for tooth landmark detection in 3D IOS. Unlike traditional two-stage workflows that segment teeth before detecting landmarks, CHaRNet directly operates on the input point cloud, thus reducing complexity and computational overhead. Our method integrates four modules: (1) a point cloud encoder, (2) a point cloud decoder with a heatmap regression head, (3) a teeth presence classification head, and (4) the novel Conditioned Heatmap Regression (CHaR) module. By leveraging teeth presence classification, the CHaR module dynamically adapts to missing teeth and enhances detection accuracy in complex dental models. We evaluate CHaRNet using five point cloud learning algorithms on a clinical dataset of 1,214 annotated 3D models. Both the dataset and code will be publicly released to address the lack of open datasets in orthodontics and inspire further research. CHaRNet achieves a Mean Euclidean Distance Error (MEDE) of 0.51 mm on typical dental models and 1.28 mm across all dentition types, with corresponding Mean Success Rates (MSR) of 87.06% and 82.40%, respectively. Notably, it exhibits robust performance on irregular geometries, including models with missing teeth. This end-to-end approach streamlines orthodontic workflows, enhances 3D IOS analysis precision, and supports efficient computer-assisted treatment planning.