用扩散模型生成个性牙冠，真实修复效果好。
From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion
- 基于合成数据训练扩散模型，根据局部解剖结构生成牙冠。
- 合成损伤测试下IoU达81.8%，点云距离仅0.00034。
- 可直接用于真实病例，避免咬合干扰，适合临床修复。
我们提出ToothCraft，一种基于扩散模型的牙冠上下文生成方法，训练数据来自人工构造的不完整牙齿。依托近期条件化3D形状扩散模型进展，该模型能根据局部解剖结构自动完成牙冠重建。为解决训练数据匮乏问题，我们设计了一套增强流程，从公开完整的牙列数据集（3DS, ODD）中生成多种不完整齿形。通过合成多样化训练样本，模型在各类牙体缺损上均表现出强鲁棒性。实验表明，模型在合成损伤测试中达到81.8%的交并比（IoU）和0.00034的切比雪夫距离（CD）。此外，模型可直接应用于真实病例，生成牙冠与对颌牙无明显重叠，有效降低咬合干扰风险。代码、模型权重及数据信息将公开于：https://github.com/ikarus1211/VISAPP_ToothCraft
原文摘要 · Abstract (English)
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
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