From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion

TL;DR

ToothCraft is a diffusion-based model trained on synthetic incomplete teeth data that accurately reconstructs complete tooth crowns, demonstrating effectiveness on real-world dental restorations.

Contribution

The paper introduces a novel diffusion model for tooth crown completion trained on synthetically augmented data, enabling robust real-world application.

Findings

Achieved 81.8% IoU on synthetically damaged teeth

Chamfer Distance of 0.00034 indicates high reconstruction accuracy

Model effectively reduces occlusal interference in real cases

Abstract

We present ToothCraft, a diffusion-based model for the contextual generation of tooth crowns, trained on artificially created incomplete teeth. Building upon recent advancements in conditioned diffusion models for 3D shapes, we developed a model capable of an automated tooth crown completion conditioned on local anatomical context. To address the lack of training data for this task, we designed an augmentation pipeline that generates incomplete tooth geometries from a publicly available dataset of complete dental arches (3DS, ODD). By synthesising a diverse set of training examples, our approach enables robust learning across a wide spectrum of tooth defects. Experimental results demonstrate the strong capability of our model to reconstruct complete tooth crowns, achieving an intersection over union (IoU) of 81.8% and a Chamfer Distance (CD) of 0.00034 on synthetically damaged testing restorations. Our experiments demonstrate that the model can be applied directly to real-world cases, effectively filling in incomplete teeth, while generated crowns show minimal intersection with the opposing dentition, thus reducing the risk of occlusal interference. Access to the code, model weights, and dataset information will be available at: https://github.com/ikarus1211/VISAPP_ToothCraft