3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation

Ananya Jana, Hrebesh Molly Subhash, Dimitris N. Metaxas

Abstract:

Manual tooth segmentation of 3D tooth meshes is tedious
and there is variations among dentists. 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