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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2210.06094 |
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| _version_ | 1866914234268385280 |
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| author | Ben-Hamadou, Achraf Neifar, Nour Rekik, Ahmed Smaoui, Oussama Bouzguenda, Firas Pujades, Sergi Boyer, Edmond Ladroit, Edouard |
| author_facet | Ben-Hamadou, Achraf Neifar, Nour Rekik, Ahmed Smaoui, Oussama Bouzguenda, Firas Pujades, Sergi Boyer, Edmond Ladroit, Edouard |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_06094 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis Ben-Hamadou, Achraf Neifar, Nour Rekik, Ahmed Smaoui, Oussama Bouzguenda, Firas Pujades, Sergi Boyer, Edmond Ladroit, Edouard Computer Vision and Pattern Recognition 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 |
| title | Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2210.06094 |