[2603.26588] From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.26588 (cs) [Submitted on 27 Mar 2026] Title:From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion Authors:Dávid Pukanec, Tibor Kubík, Michal Španěl View a PDF of the paper titled From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion, by D\'avid Pukanec and 2 other authors View PDF HTML (experimental) 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 fillin...