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

翻译：本文提出ToothCraft——一种基于扩散模型的牙冠上下文生成模型，该模型在人工生成的不完整牙齿数据上进行训练。基于3D形状条件扩散模型的最新进展，我们开发了一种能够基于局部解剖上下文自动完成牙冠生成的模型。为解决此任务训练数据不足的问题，我们设计了一种数据增强流水线，能够从公开的完整牙弓数据集（3DS, ODD）中生成不完整牙齿几何结构。通过合成多样化的训练样本，该方法使模型能够在广泛的牙齿缺损场景中实现鲁棒学习。实验结果表明，该模型在重建完整牙冠方面表现出色，在合成损伤的测试修复体上达到了81.8%的交并比（IoU）和0.00034的倒角距离（CD）。实验证明，该模型可直接应用于真实临床案例，有效填充不完整牙齿，同时生成的牙冠与对颌牙列的交叉量极小，从而降低咬合干扰风险。代码、模型权重及数据集信息将开放获取于：https://github.com/ikarus1211/VISAPP_ToothCraft