# Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis

arXiv: https://arxiv.org/abs/2210.06094 · HTML: https://mlsanity.com/paper/2210.06094/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 2 of 2)

- [Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation](https://mlsanity.com/paper/2204.08797/) (2022)
- [Automatic Recognition of Landmarks on Digital Dental Models](https://mlsanity.com/paper/2012.12946/) (2020)

## Built on by (most cited first, 11 of 11)

- [An Implicit Parametric Morphable Dental Model](https://mlsanity.com/paper/2211.11402/) (2022)
- [3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation](https://mlsanity.com/paper/2301.10531/) (2023)
- [AlphaDent: A dataset for automated tooth pathology detection](https://mlsanity.com/paper/2507.22512/) (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)
- [CHaRM: Conditioned Heatmap Regression Methodology for Accurate and Fast Dental Landmark Localization](https://mlsanity.com/paper/2501.13073/) (2025)
- [Text Condition Embedded Regression Network for Automated Dental Abutment Design](https://mlsanity.com/paper/2511.22578/) (2025)
- [Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge](https://mlsanity.com/paper/2512.08323/) (2025)
- [CrownGen: Patient-customized Crown Generation via Point Diffusion Model](https://mlsanity.com/paper/2512.21890/) (2025)
- [OrthoAI v2: From Single-Agent Segmentation to Dual-Agent Treatment Planning for Clear Aligners](https://mlsanity.com/paper/2603.15663/) (2026)
- [Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design](https://mlsanity.com/paper/2604.09047/) (2026)
- [ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations](https://mlsanity.com/paper/2607.12335/) (2026)

## Abstract (arXiv)

Intraoral 3D scanning is now widely adopted in modern dentistry and plays a central role in supporting key tasks such as tooth segmentation, detection, labeling, and dental landmark identification. Accurate analysis of these scans is essential for orthodontic and restorative treatment planning, as it enables automated workflows and minimizes the need for manual intervention. However, the development of robust learning-based solutions remains challenging due to the limited availability of high-quality public datasets and standardized benchmarks. This article presents Teeth3DS+, an extended public benchmark dedicated to intraoral 3D scan analysis. Developed in the context of the MICCAI 3DTeethSeg and 3DTeethLand challenges, Teeth3DS+ supports multiple fundamental tasks, including tooth detection, segmentation, labeling, 3D modeling, and dental landmark identification. The dataset consists of rigorously curated intraoral scans acquired using state-of-the-art scanners and validated by experienced orthodontists and dental surgeons. In addition to the data, Teeth3DS+ provides standardized data splits and evaluation protocols to enable fair and reproducible comparison of methods, with the goal of fostering progress in learning-based analysis of 3D dental scans. Detailed instructions for accessing the dataset are available at https://crns-smartvision.github.io/teeth3ds