  Manual tooth segmentation of 3D tooth meshes is tedious and there is
variations among dentists. %Manual tooth annotation of 3D tooth meshes is a
tedious task. Several deep learning based methods have been proposed to perform
automatic tooth mesh segmentation. Many of the proposed tooth mesh segmentation
algorithms summarize the mesh cell as - the cell center or barycenter, the
normal at barycenter, the cell vertices and the normals at the cell vertices.
Summarizing of the mesh cell/triangle in this manner imposes an implicit
structural constraint and makes it difficult to work with multiple resolutions
which is done in many point cloud based deep learning algorithms. We propose a
novel segmentation method which utilizes only the barycenter and the normal at
the barycenter information of the mesh cell and yet achieves competitive
performance. We are the first to demonstrate that it is possible to relax the
implicit structural constraint and yet achieve superior segmentation
performance
