TAD-Net: tooth axis detection network based on rotation transformation encoding

The tooth axes, defined on 3D tooth model, play a key role in digital orthodontics, which is usually used as an important reference in automatic tooth arrangement and anomaly detection. In this paper, we propose an automatic deep learning network (TAD-Net) of tooth axis detection based on rotation transformation encoding. By utilizing quaternion transformation, we convert the geometric rotation transformation of the tooth axes into the feature encoding of the point cloud of 3D tooth models. Furthermore, the feature confidence-aware attention mechanism is adopted to generate dynamic weights for the features of each point to improve the network learning accuracy. Experimental results show that the proposed method has achieved higher detection accuracy on the constructed dental data set compared with the existing networks.