用点云扩散模型自动生成个性化牙冠，大幅减少设计时间。
CrownGen: Patient-customized Crown Generation via Point Diffusion Model
- 基于牙齿级点云的去噪扩散模型生成牙冠形态。
- 在496个扫描数据上验证，几何保真度优于现有方法。
- 临床评估显示效果不输人工专家，适合快速修复场景。
数字化牙冠设计在修复牙科中仍为耗时瓶颈。本文提出CrownGen，一种基于新型牙齿级点云表示的生成框架，利用去噪扩散模型实现患者定制化牙冠自动化设计。系统包含两个核心模块：边界预测模块用于建立空间先验，扩散生成模块可在一次推理中合成多颗牙齿的高保真形态。我们在496个外部扫描数据上进行量化基准测试，并对26例修复案例开展临床研究。结果表明，CrownGen在几何保真度上超越现有最优模型，显著降低主动设计时间。经训练牙医评估，CrownGen辅助生成的牙冠在质量上与专家手工流程产出无统计学差异。该方法通过自动化复杂修复建模，提供可扩展解决方案，有助于降低成本、缩短交付周期，提升患者获取高质量牙科服务的可及性。
原文摘要 · Abstract (English)
Digital crown design remains a labor-intensive bottleneck in restorative dentistry. We present CrownGen, a generative framework that automates patient-customized crown design using a denoising diffusion model on a novel tooth-level point cloud representation. The system employs two core components: a boundary prediction module to establish spatial priors and a diffusion-based generative module to synthesize high-fidelity morphology for multiple teeth in a single inference pass. We validated CrownGen through a quantitative benchmark on 496 external scans and a clinical study of 26 restoration cases. Results demonstrate that CrownGen surpasses state-of-the-art models in geometric fidelity and significantly reduces active design time. Clinical assessments by trained dentists confirmed that CrownGen-assisted crowns are statistically non-inferior in quality to those produced by expert technicians using manual workflows. By automating complex prosthetic modeling, CrownGen offers a scalable solution to lower costs, shorten turnaround times, and enhance patient access to high-quality dental care.
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