# TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer

arXiv: https://arxiv.org/abs/2311.13234 · HTML: https://mlsanity.com/paper/2311.13234/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [PVT: Point-Voxel Transformer for Point Cloud Learning](https://mlsanity.com/paper/2108.06076/) (2021)
- [TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation](https://mlsanity.com/paper/2012.13697/) (2020)

## Built on by (most cited first, 10 of 10)

- [DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice](https://mlsanity.com/paper/2509.23344/) (2025)
- [3D Dental Model Segmentation with Geometrical Boundary Preserving](https://mlsanity.com/paper/2503.23702/) (2025)
- [When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels](https://mlsanity.com/paper/2409.01691/) (2024)
- [DentalBench: Benchmarking and Advancing LLMs Capability for Bilingual Dentistry Understanding](https://mlsanity.com/paper/2508.20416/) (2025)
- [Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation](https://mlsanity.com/paper/2509.07923/) (2025)
- [3DTeethSAM: Taming SAM2 for 3D Teeth Segmentation](https://mlsanity.com/paper/2512.11557/) (2025)
- [IOSVLM: A 3D Vision-Language Model for Unified Dental Diagnosis from Intraoral Scans](https://mlsanity.com/paper/2603.16781/) (2026)
- [UniDCF: A Foundation Model for Comprehensive Dentocraniofacial Hard Tissue Reconstruction](https://mlsanity.com/paper/2508.11728/) (2025)
- [CrownGen: Patient-customized Crown Generation via Point Diffusion Model](https://mlsanity.com/paper/2512.21890/) (2025)
- [TCATSeg: A Tooth Center-Wise Attention Network for 3D Dental Model Semantic Segmentation](https://mlsanity.com/paper/2603.16620/) (2026)

## Abstract (arXiv)

Optical Intraoral Scanners (IOS) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva. Accurate 3D tooth segmentation in IOSs is critical for various dental applications, while previous methods are error-prone at complicated boundaries and exhibit unsatisfactory results across patients. In this paper, we propose TSegFormer which captures both local and global dependencies among different teeth and the gingiva in the IOS point clouds with a multi-task 3D transformer architecture. Moreover, we design a geometry-guided loss based on a novel point curvature to refine boundaries in an end-to-end manner, avoiding time-consuming post-processing to reach clinically applicable segmentation. In addition, we create a dataset with 16,000 IOSs, the largest ever IOS dataset to the best of our knowledge. The experimental results demonstrate that our TSegFormer consistently surpasses existing state-of-the-art baselines. The superiority of TSegFormer is corroborated by extensive analysis, visualizations and real-world clinical applicability tests. Our code is available at https://github.com/huiminxiong/TSegFormer.