使用口腔内图像进行3D牙冠重建的高保真框架.
Ni Liao1, Xinyu Zhang1, Wenkang Chen2
1Department of Orthodontics, College & Hospital of Stomatology, Guangxi Medical University, Nanning, Guangxi, 530021, China.
International dental journal
|January 28, 2026
概括
DenGaussDiff使用高斯神经场和扩散模型从最小的口内图像精确地重建3D牙冠. 该框架增强了数字牙科,以改善正统牙科诊断和监测.
科学领域:
- 生物医学工程 生物医学工程
- 计算机视觉 计算机视觉
- 牙科技术 牙科技术
背景情况:
- 精确的3D牙冠重建对于诊断和治疗规划至关重要.
- 现有的方法在使用有限的口内图像时难以准确.
研究的目的:
- 开发DenGaussDiff,一个用于精确3D牙冠重建的新框架.
- 使用三维 (3D) 高斯神经场和扩散先验.
- 从仅有五张口腔内图像中重建牙冠.
主要方法:
- 收集了1000个临床病例,包括5个口内图像和口内扫描 (IOS) 模型.
- 采用图像细分,增强和ControlNet++用于3D表示.
- 使用多尺度高斯染和代最接近点 (ICP) 注册优化3D模型.
主要成果:
- 丹高斯迪夫的性能优于现有方法,PSNR提高了1.33%,SSIM提高了3.9%.
- 在重建和IOS模型之间实现了高精度的注册.
- 在公共数据集上表现出色,用于检测牙科问题.
结论:
- 丹高斯迪夫 (DenGaussDiff) 能够从稀疏的口内图像中精确的3D牙冠重建.
- 显示 Orthodontic 诊断,监测和数字牙科实施的潜力.
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