CrownGen: Patient-customized Crown Generation via Point Diffusion Model
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arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918268212609024 |
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| author | Bae, Juyoung Son, Moo Hyun Peng, Jiale Qu, Wanting Chen, Wener Qiu, Zelin Li, Kaixin Chen, Xiaojuan Lin, Yifan Chen, Hao |
| author_facet | Bae, Juyoung Son, Moo Hyun Peng, Jiale Qu, Wanting Chen, Wener Qiu, Zelin Li, Kaixin Chen, Xiaojuan Lin, Yifan Chen, Hao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21890 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CrownGen: Patient-customized Crown Generation via Point Diffusion Model Bae, Juyoung Son, Moo Hyun Peng, Jiale Qu, Wanting Chen, Wener Qiu, Zelin Li, Kaixin Chen, Xiaojuan Lin, Yifan Chen, Hao Computer Vision and Pattern Recognition 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. |
| title | CrownGen: Patient-customized Crown Generation via Point Diffusion Model |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.21890 |