  Optical Intra-oral Scanners (IOS) are widely used in digital dentistry,
providing 3-Dimensional (3D) and high-resolution geometrical information of
dental crowns and the gingiva. Accurate 3D tooth segmentation, which aims to
precisely delineate the tooth and gingiva instances in IOS, plays a critical
role in a variety of dental applications. However, segmentation performance of
previous methods are error-prone in complicated tooth-tooth or tooth-gingiva
boundaries, and usually exhibit unsatisfactory results across various patients,
yet the clinically applicability is not verified with large-scale dataset. In
this paper, we propose a novel method based on 3D transformer architectures
that is evaluated with large-scale and high-resolution 3D IOS datasets. Our
method, termed TFormer, captures both local and global dependencies among
different teeth to distinguish various types of teeth with divergent anatomical
structures and confusing boundaries. Moreover, we design a geometry guided loss
based on a novel point curvature to exploit boundary geometric features, which
helps refine the boundary predictions for more accurate and smooth
segmentation. We further employ a multi-task learning scheme, where an
additional teeth-gingiva segmentation head is introduced to improve the
performance. Extensive experimental results in a large-scale dataset with
16,000 IOS, the largest IOS dataset to our best knowledge, demonstrate that our
TFormer can surpass existing state-of-the-art baselines with a large margin,
with its utility in real-world scenarios verified by a clinical applicability
test.
