TCATSeg: A Tooth Center-Wise Attention Network for 3D Dental Model Semantic Segmentation

TL;DR

TCATSeg is a novel neural network framework that improves 3D dental model segmentation by integrating local geometry with global context using superpoints, validated on a new dataset.

Contribution

We introduce TCATSeg, a new method combining local features and global semantic context with superpoints for improved 3D dental segmentation.

Findings

Outperforms existing methods in accuracy

Effective on a new dataset of 400 dental models

Enhances segmentation of complex tooth arrangements

Abstract

Accurate semantic segmentation of 3D dental models is essential for digital dentistry applications such as orthodontics and dental implants. However, due to complex tooth arrangements and similarities in shape among adjacent teeth, existing methods struggle with accurate segmentation, because they often focus on local geometry while neglecting global contextual information. To address this, we propose TCATSeg, a novel framework that combines local geometric features with global semantic context. We introduce a set of sparse yet physically meaningful superpoints to capture global semantic relationships and enhance segmentation accuracy. Additionally, we present a new dataset of 400 dental models, including pre-orthodontic samples, to evaluate the generalization of our method. Extensive experiments demonstrate that TCATSeg outperforms state-of-the-art approaches.