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Optical Intraoral Scanners (IOS) are widely used in digital dentistry toprovide detailed 3D information of dental crowns and the gingiva. Accurate 3Dtooth segmentation in IOSs is critical for various dental applications, whileprevious methods are error-prone at complicated boundaries and exhibitunsatisfactory results across patients. In this paper, we propose TSegFormerwhich captures both local and global dependencies among different teeth and thegingiva 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 torefine boundaries in an end-to-end manner, avoiding time-consumingpost-processing to reach clinically applicable segmentation. In addition, wecreate a dataset with 16,000 IOSs, the largest ever IOS dataset to the best ofour knowledge. The experimental results demonstrate that our TSegFormerconsistently surpasses existing state-of-the-art baselines. The superiority ofTSegFormer is corroborated by extensive analysis, visualizations and real-worldclinical applicability tests. Our code is available athttps://github.com/huiminxiong/TSegFormer.

TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer