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CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2024-09-29
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Accurate estimation of 6-DoF tooth pose in 3D intraoral 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 6-DoF tooth pose in 3D intraoral scans for dental applications using deep learning[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(9): 1240-1249.
@article{title="Accurate estimation of 6-DoF tooth pose in 3D intraoral 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="25",
number="9",
pages="1240-1249",
year="2024",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2300596"
}
%0 Journal Article
%T Accurate estimation of 6-DoF tooth pose in 3D intraoral 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 Frontiers of Information Technology & Electronic Engineering
%V 25
%N 9
%P 1240-1249
%@ 2095-9184
%D 2024
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2300596
TY - JOUR
T1 - Accurate estimation of 6-DoF tooth pose in 3D intraoral 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 - Frontiers of Information Technology & Electronic Engineering
VL - 25
IS - 9
SP - 1240
EP - 1249
%@ 2095-9184
Y1 - 2024
PB - Zhejiang University Press & Springer
ER -
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, 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°–5.979° 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°, which is acceptable for many clinical and industrial applications.
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