  Teeth segmentation is an important topic in dental restorations that is
essential for crown generation, diagnosis, and treatment planning. In the
dental field, the variability of input data is high and there are no publicly
available 3D dental arch datasets. Although there has been improvement in the
field provided by recent deep learning architectures on 3D data, there still
exists some problems such as properly identifying missing teeth in an arch. We
propose to use spectral clustering as a self-supervisory signal to joint-train
neural networks for segmentation of 3D arches. Our approach is motivated by the
observation that K-means clustering provides cues to capture margin lines
related to human perception. The main idea is to automatically generate
training data by decomposing unlabeled 3D arches into segments relying solely
on geometric information. The network is then trained using a joint loss that
combines a supervised loss of annotated input and a self-supervised loss of
non-labeled input. Our collected data has a variety of arches including arches
with missing teeth. Our experimental results show improvement over the fully
supervised state-of-the-art MeshSegNet when using semi-supervised learning.
Finally, we contribute code and a dataset.
