# Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge

arXiv: https://arxiv.org/abs/2512.08323 · HTML: https://mlsanity.com/paper/2512.08323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework](https://mlsanity.com/paper/2202.07123/) (2022)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations](https://mlsanity.com/paper/2002.12687/) (2020)
- [Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans](https://mlsanity.com/paper/2109.11941/) (2021)
- [An Adjustable Farthest Point Sampling Method for Approximately-sorted Point Cloud Data](https://mlsanity.com/paper/2208.08795/) (2022)
- [3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge](https://mlsanity.com/paper/2305.18277/) (2023)
- [Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis](https://mlsanity.com/paper/2210.06094/) (2022)
- [Automatic Recognition of Landmarks on Digital Dental Models](https://mlsanity.com/paper/2012.12946/) (2020)
- [Dense Representative Tooth Landmark/axis Detection Network on 3D Model](https://mlsanity.com/paper/2111.04212/) (2021)
- [MICCAI STS 2024 Challenge: Semi-Supervised Instance-Level Tooth Segmentation in Panoramic X-ray and CBCT Images](https://mlsanity.com/paper/2511.22911/) (2025)
- [Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry](https://mlsanity.com/paper/2504.11418/) (2025)

## Abstract (arXiv)

Teeth landmark detection is a key task in modern orthodontics, supporting advanced diagnosis, personalized treatment planning, and effective monitoring of treatment progress. However, several significant challenges may arise due to the intricate geometry of individual teeth and the substantial variations observed across different individuals. To address these complexities, the development of advanced techniques, especially through the application of deep learning, is essential for the precise and reliable detection of 3D tooth landmarks. In this context, the 3DTeethLand challenge was held in conjunction with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2024, calling for algorithms focused on teeth landmark detection from intraoral 3D scans. This challenge introduced a publicly available dataset for 3D dental landmark detection from 340 intraoral scans, providing a standardized benchmark to evaluate state-of-the-art approaches and encouraging methodological advances toward addressing this clinically problem. A total of 49 teams participated, and 6 teams reached the final phase. The winning team achieved a rank score of 0.91, with a mean Average Precision of 0.78 and a mean Average Recall of 0.65, demonstrating a balance between precision and recall. Top teams achieved high precision with different strategies: the first-ranked team used a two-stage Stratified Transformer with segmentation and weighted DBSCAN, while the second-ranked team adopted a single-stage DGCNN with offset regression and class-specific non-maximum suppression.