Accurate estimation of 6-DoF tooth pose in 3D intraoral scans for dental applications using deep learning

A critical step in digital dentistry is to accurately and automatically characterize the orientation and position of individual teeth, which can subsequently be used for treatment planning and simulation in orthodontic tooth alignment. This problem remains challenging because the geometric features of different teeth are complicated and vary significantly, while a reliable large-scale dataset is yet to be constructed. In this paper we propose a novel method for automatic tooth orientation estimation by formulating it as a six-degree-of-freedom (6-DoF) tooth pose estimation task. Regarding each tooth as a three-dimensional (3D) point cloud, we design a deep neural network with a feature extractor backbone and a two-branch estimation head for tooth pose estimation. Our model, trained with a novel loss function on the newly collected large-scale dataset (10 393 patients with 280 611 intraoral tooth scans), achieves an average Euler angle error of only 4.780 degrees-5.979 degrees and a translation L1 error of 0.663 mm on a hold-out set of 2598 patients (77 870 teeth). Comprehensive experiments show that 98.29% of the estimations produce a mean angle error of less than 15 degrees, which is acceptable for many clinical and industrial applications.