3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation

A vital task in computer aided orthodontic treatment is automated and accurate segmentation of teeth from Manual tooth segmentation of 3D tooth meshes is tedious intraoral scans. The intraoral scanners produce 3D surface and there is variations among dentists. Several deep learning reconstructions of the teeth either in the form of point cloud based methods have been proposed to perform automatic or in a mesh format or both. A highly accurate automated tooth mesh segmentation. Many of the proposed tooth mesh tooth mesh segmentation can help in downstream tasks such segmentation algorithms summarize the mesh cell as - the cell as recognising and classifying different dental/oral conditions center or barycenter, the normal at barycenter, the cell vertices like gingivitis, caries, and white lesions. There are multiple and the normals at the cell vertices. Summarizing of the mesh challenges involved in tooth mesh segmentation such as - cell/triangle in this manner imposes an implicit structural constraint crowded teeth, misaligned teeth, missing teeth. The size and and makes it difficult to work with multiple resolutions shape of teeth can also vary widely across subjects. The second which is done in many point cloud based deep learning algorithms.