# 3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation

arXiv: https://arxiv.org/abs/2301.10531 · HTML: https://mlsanity.com/paper/2301.10531/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 25 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [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)
- [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020)
- [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://mlsanity.com/paper/2202.07123/) (2022)
- [Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis](https://mlsanity.com/paper/2105.01288/) (2021)
- [Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion](https://mlsanity.com/paper/2103.07074/) (2021)
- [TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation](https://mlsanity.com/paper/2012.13697/) (2020)
- [Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis](https://mlsanity.com/paper/2210.06094/) (2022)
- [Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Model](https://mlsanity.com/paper/2209.08132/) (2022)

## Built on by (most cited first, 4 of 4)

- [3D Dental Model Segmentation with Geometrical Boundary Preserving](https://mlsanity.com/paper/2503.23702/) (2025)
- [A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans](https://mlsanity.com/paper/2305.00244/) (2023)
- [Evaluating the Suitability of Different Intraoral Scan Resolutions for Deep Learning-Based Tooth Segmentation](https://mlsanity.com/paper/2502.19515/) (2025)
- [Prompt-Based Caption Generation for Single-Tooth Dental Images Using Vision-Language Models](https://mlsanity.com/paper/2603.07403/) (2026)

## Abstract (arXiv)

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