TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry
  Guided Transformer
Huimin Xiong Kunle Li Kaiyuan Tan Yang Feng Joey Tianyi Zhou Jin Hao Haochao Ying Jian Wu Zuozhu Liu
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.