{"ID":2824949,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.21890","arxiv_id":"2512.21890","title":"CrownGen: Patient-customized Crown Generation via Point Diffusion Model","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.","short_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 predictio...","url_abs":"https://arxiv.org/abs/2512.21890","url_pdf":"https://arxiv.org/pdf/2512.21890v2","authors":"[\"Juyoung Bae\",\"Moo Hyun Son\",\"Jiale Peng\",\"Wanting Qu\",\"Wener Chen\",\"Zelin Qiu\",\"Kaixin Li\",\"Xiaojuan Chen\",\"Yifan Lin\",\"Hao Chen\"]","published":"2025-12-26T06:40:33Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}