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: this https URL
Abstract (translated)
我们提出了ToothCraft，这是一种基于扩散模型的上下文感知牙冠生成方法，该模型在人工生成的不完整牙齿数据上进行了训练。基于近期条件扩散模型在3D形状领域的进展，我们开发了一种能够根据局部解剖学上下文自动完成牙冠修复的模型。针对该任务训练数据匮乏的问题，我们设计了一个增强流程，可从公开的完整牙弓数据集（3DS、ODD）中生成不完整的牙齿几何体。通过合成多样化的训练样本，我们的方法能够对广泛类型的牙齿缺陷进行鲁棒学习。实验结果表明，该模型在重建完整牙冠方面表现出色，在合成损伤测试修复体上实现了81.8%的交并比（IoU）和0.00034的倒角距离（CD）。我们的实验证明该模型可直接应用于实际病例，有效填补不完整牙齿，同时生成的牙冠与对颌牙列交集极小，从而降低了咬合干扰风险。代码、模型权重及数据集信息详见：此https链接
URL
https://arxiv.org/abs/2603.26588
