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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.08323 |
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| _version_ | 1866915963609284608 |
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| author | Ben-Hamadou, Achraf Neifar, Nour Rekik, Ahmed Smaoui, Oussama Bouzguenda, Firas Pujades, Sergi van Nistelrooij, Niels Vinayahalingam, Shankeeth Shi, Kaibo Jin, Hairong Zheng, Youyi Kubík, Tibor Kodym, Oldřich Šilling, Petr Trávníčková, Kateřina Mojžiš, Tomáš Matula, Jan Hartanto, Jeffry Zhu, Xiaoying Nguyen, Kim-Ngan Dascalu, Tudor Wu, Huikai Liu, and Weijie Zhuang, Shaojie Wei, Guangshun Zhou, Yuanfeng |
| author_facet | Ben-Hamadou, Achraf Neifar, Nour Rekik, Ahmed Smaoui, Oussama Bouzguenda, Firas Pujades, Sergi van Nistelrooij, Niels Vinayahalingam, Shankeeth Shi, Kaibo Jin, Hairong Zheng, Youyi Kubík, Tibor Kodym, Oldřich Šilling, Petr Trávníčková, Kateřina Mojžiš, Tomáš Matula, Jan Hartanto, Jeffry Zhu, Xiaoying Nguyen, Kim-Ngan Dascalu, Tudor Wu, Huikai Liu, and Weijie Zhuang, Shaojie Wei, Guangshun Zhou, Yuanfeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_08323 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge Ben-Hamadou, Achraf Neifar, Nour Rekik, Ahmed Smaoui, Oussama Bouzguenda, Firas Pujades, Sergi van Nistelrooij, Niels Vinayahalingam, Shankeeth Shi, Kaibo Jin, Hairong Zheng, Youyi Kubík, Tibor Kodym, Oldřich Šilling, Petr Trávníčková, Kateřina Mojžiš, Tomáš Matula, Jan Hartanto, Jeffry Zhu, Xiaoying Nguyen, Kim-Ngan Dascalu, Tudor Wu, Huikai Liu, and Weijie Zhuang, Shaojie Wei, Guangshun Zhou, Yuanfeng Computer Vision and Pattern Recognition 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. |
| title | Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2512.08323 |