Segmentation-Guided Semi-Supervised Landmark Detection on Intraoral 3D Scans

Detecting anatomical tooth landmarks on intraoral 3D scans (IOS) is a key step in digital orthodontics, but supervised methods are limited by the few scans carrying landmark annotations. On the public Teeth3DS+ benchmark every scan has per-vertex tooth segmentation and FDI labels, whereas only a small subset also carries dental landmarks, so landmark detection becomes a semi-supervised problem in which abundant segmentation labels compensate for the missing landmark labels. We pretrain a shared point-cloud encoder on segmentation using all non-test scans, then train a landmark head with an equivariance consistency loss and confidence-filtered pseudo-labels on the unlabelled scans, plus a geometric prior that constrains each predicted landmark to the segmented region of its tooth. All model selection uses a held-out validation split; the test set serves only for final reporting. On the 3DTeethLand test split the method raises the mean Average Precision from 0.702 to 0.753 and lowers the mean radial error from 0.63 to 0.54 mm over a supervised baseline with the same labelled budget, ranking third of seven systems. Existing segmentation labels are thus an effective, low-cost signal for label-efficient 3D landmark detection.