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Accurate estimation of 6D tooth pose in 3D intra oral scans for dental applications using deep learning
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Wanghui DING, Kaiwei SUN, Mengfei YU, Hangzheng LIN,Yang FENG, Jianhua LI, Zuozhu LIU. Accurate estimation of 6D tooth pose in 3D intra oral scans for dental applications using deep learning[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .
@article{title="Accurate estimation of 6D tooth pose in 3D intra oral scans for dental applications using deep learning",
author="Wanghui DING, Kaiwei SUN, Mengfei YU, Hangzheng LIN,Yang FENG, Jianhua LI, Zuozhu LIU",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300596"
}
%0 Journal Article
%T Accurate estimation of 6D tooth pose in 3D intra oral scans for dental applications using deep learning
%A Wanghui DING
%A Kaiwei SUN
%A Mengfei YU
%A Hangzheng LIN
%A Yang FENG
%A Jianhua LI
%A Zuozhu LIU
%J Journal of Zhejiang University SCIENCE C
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%N -1
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%@ 2095-9184
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300596
TY - JOUR
T1 - Accurate estimation of 6D tooth pose in 3D intra oral scans for dental applications using deep learning
A1 - Wanghui DING
A1 - Kaiwei SUN
A1 - Mengfei YU
A1 - Hangzheng LIN
A1 - Yang FENG
A1 - Jianhua LI
A1 - Zuozhu LIU
J0 - Journal of Zhejiang University Science C
VL - -1
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%@ 2095-9184
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2300596
Abstract: A critical step in digital dentistry is to accurately and automatically characterize the orientation and position of individual teeth; this 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. This paper proposes a novel method for automatic tooth orientation estimation by formulating it as a six degrees-of-freedom (6D) 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 4.78-5.979° and a translation L-1 error of 0.663 mm on a hold-out set of 2598 patients (77,870 teeth). The comprehensive experiments further show that 98.29% of the estimations produce a mean angle error of less than 15°, which is acceptable for many clinical and industrial applications. To the best of our knowledge, we present the first work for automatic 6D tooth pose estimation using deep learning, demonstrating the strong potential of artificial intelligence in future digital dentistry.
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