CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2024-09-29
Cited: 0
Clicked: 1331
Citations: Bibtex RefMan EndNote GB/T7714
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.
[1]Cai M, Reid I, 2020. Reconstruct locally, localize globally: a model free method for object pose estimation. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p. 3150-3160.
[2]Chen QM, Wang YH, Shuai J, 2023. Current status and future prospects of stomatology research. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 24(10):853-867.
[3]Gu CH, Ren XF, 2010. Discriminative mixture-of-templates for viewpoint classification. 11th European Conf on Computer Vision, p.408-421.
[4]Herrmann W, 1967. On the completion of Fédération Dentaire Internationale Specifications. Zahn Mitteil, 57(23):1147-1149 (in German).
[5]Hinterstoisser S, Cagniart C, Ilic S, et al., 2012. Gradient response maps for real-time detection of textureless objects. IEEE Trans Patt Anal Mach Intell, 34(5):876-888.
[6]Kendall A, Grimes M, Cipolla R, 2015. PoseNet: a convolutional network for real-time 6-DOF camera relocalization. Proc IEEE Int Conf on Computer Vision, p.2938-2946.
[7]Li ZG, Wang G, Ji XY, 2019. CDPN: coordinates-based disentangled pose network for real-time RGB-based 6-DoF object pose estimation. Proc IEEE/CVF Int Conf on Computer Vision, p.7677-7686.
[8]Liebelt J, Schmid C, Schertler K, 2008. Viewpoint-independent object class detection using 3D feature maps. IEEE Conf on Computer Vision and Pattern Recognition, p.1-8.
[9]Mok V, Ong SH, Foong KWC, et al., 2002. Pose estimation of teeth through crown-shape matching. Proc SPIE 4684, Medical Imaging 2002: Image Processing, p.955-964.
[10]Newell A, Yang KY, Deng J, 2016. Stacked hourglass networks for human pose estimation. 14th European Conf on Computer Vision, p.483-499.
[11]Oberweger M, Rad M, Lepetit V, 2018. Making deep heatmaps robust to partial occlusions for 3D object pose estimation. Proc 15th European Conf on Computer Vision, p.125-141.
[12]Park K, Patten T, Vincze M, 2019. Pix2Pose: pixel-wise coordinate regression of objects for 6D pose estimation. Proc IEEE/CVF Int Conf on Computer Vision, p.7667-7676.
[13]Peng SD, Liu Y, Huang QX, et al., 2019. PVNet: pixel-wise voting network for 6DoF pose estimation. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.4556-4565.
[14]Qi CR, Litany O, He KM, et al., 2019. Deep Hough voting for 3D object detection in point clouds. Proc IEEE/CVF Int Conf on Computer Vision, p.9276-9285.
[15]Su H, Qi CR, Li YY, et al., 2015. Render for CNN: viewpoint estimation in images using CNNs trained with rendered 3D model views. Proc IEEE Int Conf on Computer Vision, p.2686-2694.
[16]Sun M, Bradski G, Xu BX, et al., 2010. Depth-encoded Hough voting for joint object detection and shape recovery. 11th European Conf on Computer Vision, p.658-671.
[17]Ulrich J, Alsayed A, Arvin F, et al., 2022. Towards fast fiducial marker with full 6 DOF pose estimation. Proc 37th ACM/SIGAPP Symp on Applied Computing, p.723-730.
[18]Wang C, Xu DF, Zhu YK, et al., 2019. DenseFusion: 6D object pose estimation by iterative dense fusion. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.3338-3347.
[19]Wang H, Sridhar S, Huang JW, et al., 2019. Normalized object coordinate space for category-level 6D object pose and size estimation. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.2637-2646.
[20]Wang Y, Sun YB, Liu ZW, et al., 2019. Dynamic graph CNN for learning on point clouds. ACM Trans Graph, 38(5):146.
[21]Wei GD, Cui MZ, Liu YM, et al., 2020. TANet: towards fully automatic tooth arrangement. 16th European Conf on Computer Vision, p.481-497.
[22]Xiang Y, Schmidt T, Narayanan V, et al., 2018. PoseCNN: a convolutional neural network for 6D object pose estimation in cluttered scenes. Proc 14th Robotics: Science and Systems.
[23]Zhou Y, Tuzel O, 2018. VoxelNet: end-to-end learning for point cloud based 3D object detection. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.4490-4499.
[24]Zhou Y, Barnes C, Lu JW, et al., 2019. On the continuity of rotation representations in neural networks. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.5738-5746.
[25]Zhu JJ, Yang YX, Wong HM, 2023. Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 24(11):974-984.
Open peer comments: Debate/Discuss/Question/Opinion
<1>