  3D tooth segmentation is an important task for digital orthodontics. Several
Deep Learning methods have been proposed for automatic tooth segmentation from
3D dental models or intraoral scans. These methods require annotated 3D
intraoral scans. Manually annotating 3D intraoral scans is a laborious task.
One approach is to devise self-supervision methods to reduce the manual
labeling effort. Compared to other types of point cloud data like scene point
cloud or shape point cloud data, 3D tooth point cloud data has a very regular
structure and a strong shape prior. We look at how much representative
information can be learnt from a single 3D intraoral scan. We evaluate this
quantitatively with the help of ten different methods of which six are generic
point cloud segmentation methods whereas the other four are tooth segmentation
specific methods. Surprisingly, we find that with a single 3D intraoral scan
training, the Dice score can be as high as 0.86 whereas the full training set
gives Dice score of 0.94. We conclude that the segmentation methods can learn a
great deal of information from a single 3D tooth point cloud scan under
suitable conditions e.g. data augmentation. We are the first to quantitatively
evaluate and demonstrate the representation learning capability of Deep
Learning methods from a single 3D intraoral scan. This can enable building
self-supervision methods for tooth segmentation under extreme data limitation
scenario by leveraging the available data to the fullest possible extent.
