3D Dental Model Segmentation with Geometrical Boundary Preserving
Journal:
arXiv
Published Date:
Mar 31, 2025
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.
