3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation

TL;DR

This paper introduces a simplified mesh cell representation for 3D tooth segmentation, using only barycenter and normal information, which relaxes structural constraints and achieves competitive results.

Contribution

The paper proposes a novel segmentation method that simplifies mesh cell representation, enabling effective multi-resolution processing and superior performance.

Findings

Achieves competitive segmentation accuracy with simplified cell features.

First to demonstrate relaxation of structural constraints in mesh segmentation.

Outperforms existing methods in tooth mesh segmentation tasks.

Abstract

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