Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis
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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
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.
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2 of 24 references.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 5; 2026: 3.
11 papers.
- 2022An Implicit Parametric Morphable Dental Modelcited by 12
- 20233D Tooth Mesh Segmentation with Simplified Mesh Cell Representationcited by 4
- 2025AlphaDent: A dataset for automated tooth pathology detectioncited by 2
- 2023A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scanscited by 1
- 2025CHaRM: Conditioned Heatmap Regression Methodology for Accurate and Fast Dental Landmark Localizationcited by 1
- 2025Text Condition Embedded Regression Network for Automated Dental Abutment Designcited by 0
- 2025Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challengecited by 0
- 2025CrownGen: Patient-customized Crown Generation via Point Diffusion Modelcited by 0
- 2026OrthoAI v2: From Single-Agent Segmentation to Dual-Agent Treatment Planning for Clear Alignerscited by 0
- 2026Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Designcited by 0
- 2026ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activationscited by 0
- 2026ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations14 Jul 2026
- 2026Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design10 Apr 2026
- 2026OrthoAI v2: From Single-Agent Segmentation to Dual-Agent Treatment Planning for Clear Aligners10 Mar 2026
- 2025CrownGen: Patient-customized Crown Generation via Point Diffusion Model26 Dec 2025
- 2025Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge9 Dec 2025
- 2025Text Condition Embedded Regression Network for Automated Dental Abutment Design27 Nov 2025
- 2025AlphaDent: A dataset for automated tooth pathology detection30 Jul 2025
- 2025CHaRM: Conditioned Heatmap Regression Methodology for Accurate and Fast Dental Landmark Localization22 Jan 2025
- 2023A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans29 Apr 2023
- 20233D Tooth Mesh Segmentation with Simplified Mesh Cell Representation25 Jan 2023
- 2022An Implicit Parametric Morphable Dental Model21 Nov 2022