Optical Intraoral Scanners (IOS) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva. Accurate 3D tooth segmentation in IOSs is critical for various dental applications, while previous methods are error-prone at complicated boundaries and exhibit unsatisfactory results across patients. In this paper, we propose TSegFormer which captures both local and global dependencies among different teeth and the gingiva in the IOS point clouds with a multi-task 3D transformer architecture. Moreover, we design a geometry-guided loss based on a novel point curvature to refine boundaries in an end-to-end manner, avoiding time-consuming post-processing to reach clinically applicable segmentation. In addition, we create a dataset with 16,000 IOSs, the largest ever IOS dataset to the best of our knowledge. The experimental results demonstrate that our TSegFormer consistently surpasses existing state-of-the-art baselines. The superiority of TSegFormer is corroborated by extensive analysis, visualizations and real-world clinical applicability tests. Our code is available at https://github.com/huiminxiong/TSegFormer.

翻译：光学口内扫描仪（IOS）广泛应用于数字化牙科领域，可提供牙冠及牙龈的高精度三维信息。在IOS数据中实现精确的三维牙齿分割对多种牙科应用至关重要，然而现有方法在复杂边界处易产生误差，且在不同患者间的分割效果不理想。本文提出TSegFormer方法，通过多任务三维Transformer架构捕捉IOS点云中不同牙齿与牙龈间的局部及全局依赖关系。此外，我们设计了一种基于新型点曲率的几何引导损失函数，以端到端方式优化边界分割，避免耗时后处理即可达到临床适用的分割精度。同时，我们构建了包含16,000个IOS扫描数据的最大规模数据集（据我们所知）。实验结果表明，TSegFormer在各项指标上持续超越现有最优基线方法。通过大量分析、可视化结果及真实临床适用性测试，进一步验证了TSegFormer的优越性。代码开源地址：https://github.com/huiminxiong/TSegFormer。