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Manual tooth segmentation of 3D tooth meshes is tedious and there isvariations among dentists. %Manual tooth annotation of 3D tooth meshes is atedious task. Several deep learning based methods have been proposed to performautomatic tooth mesh segmentation. Many of the proposed tooth mesh segmentationalgorithms summarize the mesh cell as - the cell center or barycenter, thenormal at barycenter, the cell vertices and the normals at the cell vertices.Summarizing of the mesh cell/triangle in this manner imposes an implicitstructural constraint and makes it difficult to work with multiple resolutionswhich is done in many point cloud based deep learning algorithms. We propose anovel segmentation method which utilizes only the barycenter and the normal atthe barycenter information of the mesh cell and yet achieves competitiveperformance. We are the first to demonstrate that it is possible to relax theimplicit structural constraint and yet achieve superior segmentationperformance

3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation