3D Dental Model Segmentation with Geometrical Boundary Preserving

• URL: http://arxiv.org/abs/2503.23702v1
• Date: Mon, 31 Mar 2025 04:00:11 GMT
• Title: 3D Dental Model Segmentation with Geometrical Boundary Preserving
• Authors: Shufan Xi, Zexian Liu, Junlin Chang, Hongyu Wu, Xiaogang Wang, Aimin Hao,
• Abstract summary: 3D intraoral scan mesh is widely used in digital dentistry diagnosis, segmenting 3D intraoral scan mesh is a critical preliminary task.<n>Deep learning-based methods are capable of the high accuracy segmentation of crown.<n>However, the segmentation accuracy at the junction between the crown and the gum is still below average.
• Score: 19.232921210620447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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