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

Qiang He, Wentian Qu, Jiajia Dai, Changsong Lei, Shaofeng Wang, Feifei Zuo, Yajie Wang, Yaqian Liang, Xiaoming Deng, Cuixia Ma, Yong-Jin Liu, Hongan Wang

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.

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

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.
Paper Structure (11 sections, 2 equations, 4 figures, 3 tables)

This paper contains 11 sections, 2 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Overview
  4. Teeth Center-Wise Encoder
  5. Loss Functions
  6. Experiments
  7. Datasets and Experimental Setup
  8. Comparison with State-of-the-Arts
  9. Ablation Study
  10. Conclusion
  11. ACKNOWLEDGEMENTS

Figures (4)

  • Figure 1: SOTA method zhao2021point struggles on our TeethWild dataset.
  • Figure 2: Pipeline of TCATSeg. The encoder introduces two novel components: DPDA, which enables superpoints (TCP) to capture semantic context, and SGDA, which integrates local geometric features with global semantic context.
  • Figure 3: Tooth segmentation results on Teeth3DS and TeethWild, with errors highlighted by red dashed lines.
  • Figure 4: The comparison of superpoints in SpoTr and TCATSeg. Red points correspond to the superpoints.