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

翻译：口腔内三维扫描技术已在现代牙科诊疗中得到广泛应用，并在支持牙齿分割、检测、标记及牙科标志点识别等关键任务中发挥着核心作用。对这些扫描数据进行精确分析对于正畸与修复治疗规划至关重要，它有助于实现自动化工作流程并最大限度减少人工干预。然而，由于高质量公开数据集和标准化基准的稀缺，开发鲁棒的基于学习的解决方案仍面临挑战。本文提出了Teeth3DS+，一个专门用于口腔内三维扫描分析的扩展公共基准数据集。该数据集在MICCAI 3DTeethSeg和3DTeethLand挑战赛的背景下开发，支持多项基础任务，包括牙齿检测、分割、标记、三维建模及牙科标志点识别。数据集包含经过严格筛选的口腔内扫描数据，这些数据通过先进扫描仪获取，并由经验丰富的正畸医生和牙科外科医生验证。除数据本身外，Teeth3DS+还提供了标准化的数据划分与评估协议，以实现方法的公平且可复现的比较，其目标是推动基于学习的牙科三维扫描分析领域的发展。访问该数据集的详细说明可在 https://crns-smartvision.github.io/teeth3ds 获取。