  Artificial intelligence (AI) technology is increasingly used for digital
orthodontics, but one of the challenges is to automatically and accurately
detect tooth landmarks and axes. This is partly because of sophisticated
geometric definitions of them, and partly due to large variations among
individual tooth and across different types of tooth. As such, we propose a
deep learning approach with a labeled dataset by professional dentists to the
tooth landmark/axis detection on tooth model that are crucial for orthodontic
treatments. Our method can extract not only tooth landmarks in the form of
point (e.g. cusps), but also axes that measure the tooth angulation and
inclination. The proposed network takes as input a 3D tooth model and predicts
various types of the tooth landmarks and axes. Specifically, we encode the
landmarks and axes as dense fields defined on the surface of the tooth model.
This design choice and a set of added components make the proposed network more
suitable for extracting sparse landmarks from a given 3D tooth model. Extensive
evaluation of the proposed method was conducted on a set of dental models
prepared by experienced dentists. Results show that our method can produce
tooth landmarks with high accuracy. Our method was examined and justified via
comparison with the state-of-the-art methods as well as the ablation studies.
