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arXiv · 2512.21890

CrownGen: Patient-customized Crown Generation via Point Diffusion Model

Juyoung Bae·Moo Hyun Son·Jiale Peng·Wanting Qu·Wener Chen·Zelin Qiu·Kaixin Li·Xiaojuan Chen·Yifan Lin·Hao Chen
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Abstract

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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Juyoung Bae, Moo Hyun Son, Jiale Peng, Wanting Qu, Wener Chen, Zelin Qiu, Kaixin Li, Xiaojuan Chen, Yifan Lin, Hao Chen. 2025-12-26. CrownGen: Patient-customized Crown Generation via Point Diffusion Model. https://arxiv.org/abs/2512.21890

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