提升牙龈交界处的3D牙模分割精度
3D Dental Model Segmentation with Geometrical Boundary Preserving
- 通过选择性下采样保留牙龈交界处更多顶点
- 结合多视角图像特征，使边界分割准确率显著提升
- 适合需要高精度牙科建模的研究与临床应用
三维口内扫描网格在数字牙科诊断中广泛应用，其分割是关键预处理任务。现有深度学习方法在牙冠分割上已达到高精度，但在牙冠与牙龈交界处的分割仍不理想。传统下采样方法无法有效保留该区域的几何细节。为此，我们提出CrossTooth方法，结合3D网格选择性下采样以保留牙龈-牙冠区域更多顶点，并从多视角渲染图像中提取跨模态判别性边界特征，增强分割网络的几何表征能力。采用点网络作为主干，并融合图像互补特征，在公开口内扫描数据集上的实验表明，该方法显著提升了分割精度。
原文摘要 · Abstract (English)
3D intraoral scan mesh is widely used in digital dentistry diagnosis, segmenting 3D intraoral scan mesh is a critical preliminary task. Numerous approaches have been devised for precise tooth segmentation. Currently, the deep learning-based methods are capable of the high accuracy segmentation of crown. However, the segmentation accuracy at the junction between the crown and the gum is still below average. Existing down-sampling methods are unable to effectively preserve the geometric details at the junction. To address these problems, we propose CrossTooth, a boundary-preserving segmentation method that combines 3D mesh selective downsampling to retain more vertices at the tooth-gingiva area, along with cross-modal discriminative boundary features extracted from multi-view rendered images, enhancing the geometric representation of the segmentation network. Using a point network as a backbone and incorporating image complementary features, CrossTooth significantly improves segmentation accuracy, as demonstrated by experiments on a public intraoral scan dataset.
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