# Semi-supervised segmentation of tooth from 3D Scanned Dental Arches

arXiv: https://arxiv.org/abs/2208.05539 · HTML: https://mlsanity.com/paper/2208.05539/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 6 references that are in the map.
- First posted on arXiv 10 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Label-Efficient Learning on Point Clouds using Approximate Convex Decompositions](https://mlsanity.com/paper/2003.13834/) (2020)

## Built on by (most cited first, 3 of 3)

- [Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge](https://mlsanity.com/paper/2404.01013/) (2024)
- [Improving the quality of dental crown using a Transformer-based method](https://mlsanity.com/paper/2303.02426/) (2023)
- [Mesh based segmentation for automated margin line generation on incisors receiving crown treatment](https://mlsanity.com/paper/2507.22859/) (2025)

## Abstract (arXiv)

Teeth segmentation is an important topic in dental restorations that is essential for crown generation, diagnosis, and treatment planning. In the dental field, the variability of input data is high and there are no publicly available 3D dental arch datasets. Although there has been improvement in the field provided by recent deep learning architectures on 3D data, there still exists some problems such as properly identifying missing teeth in an arch. We propose to use spectral clustering as a self-supervisory signal to joint-train neural networks for segmentation of 3D arches. Our approach is motivated by the observation that K-means clustering provides cues to capture margin lines related to human perception. The main idea is to automatically generate training data by decomposing unlabeled 3D arches into segments relying solely on geometric information. The network is then trained using a joint loss that combines a supervised loss of annotated input and a self-supervised loss of non-labeled input. Our collected data has a variety of arches including arches with missing teeth. Our experimental results show improvement over the fully supervised state-of-the-art MeshSegNet when using semi-supervised learning. Finally, we contribute code and a dataset.