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.

翻译：数字化牙冠设计仍然是修复牙科中劳动密集型的瓶颈环节。本文提出CrownGen——一种基于新型牙齿级点云表示、利用去噪扩散模型实现患者个性化牙冠设计自动化的生成框架。该系统包含两个核心组件：用于建立空间先验的边界预测模块，以及能在单次推理过程中为多颗牙齿合成高保真形态的基于扩散的生成模块。我们通过496例外部扫描数据的定量基准测试和26例修复病例的临床研究对CrownGen进行了验证。结果表明，CrownGen在几何保真度上超越了现有最优模型，并显著缩短了主动设计时间。经专业牙医临床评估确认，CrownGen辅助设计的牙冠在质量上统计学不劣于专家技师采用人工流程制作的牙冠。通过自动化复杂修复体建模，CrownGen为降低诊疗成本、缩短周转时间、提升患者获得高质量牙科护理的可及性提供了可扩展的解决方案。