VGDM: Visual Localization-Guided 3D Dental Segmentation via Extrinsic–Intrinsic Bridging

Abstract

3D dental segmentation is a key task in digital dentistry. In real intraoral scans data (IOS), occlusion, scanning noise, and reconstruction artifacts often break down the geometric separation structure between teeth, resulting in adjacent teeth being incorrectly merged or a single tooth being over-segmented. Since existing point cloud or mesh-based methods usually rely on local neighborhood consistency, when there are spurious geometric connections, features will diffuse across instances along geometric shortcuts, resulting in instance-level error propagation. To address this issue, we propose VGDM (Visual-Guided Diffusion Modulation), which serves as a bridge between extrinsic visual localization cues and intrinsic surface features under detection settings, enabling the extrinsic cues to regulate the propagation of intrinsic surface features. Instead of global propagation on the entire intraoral scan mesh, VGDM uses the single-view 2D detection results to roughly localize the tooth region and construct local 3D surface patches based on it. Within the patch, we soft-constrain feature propagation by visual cues to suppress the cross-instance propagation generated along spurious geometric connections, and introduce a dual-stream diffusion structure to improve the overall robustness. Experimental results on the largest public intraoral dataset(Teeth3DS) show that VGDM can significantly improve the segmentation rate of tooth instances and effectively reduce the merging and over-segmentation of adjacent teeth.