Treffer: 3D reconstruction from 2D multi-view dental 2D images based on EfficientNetB0 model.
Javaid, M., Haleem, A. & Kumar, L. Current status and applications of 3D scanning in dentistry. Clin. Epidemiol. Glob. Health 7(2), 228–233 (2019).
Zhu, J. et al. Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: A preliminary study. BMC Oral Health 23(1), 358 (2023). (PMID: 3727048810239110)
Sivari, E. et al. Deep learning in diagnosis of dental anomalies and diseases: A systematic review. Diagnostics 13(15), 2512 (2023). (PMID: 3756887510416832)
Cen, Y. et al. Application of three-dimensional reconstruction technology in dentistry: A narrative review. BMC Oral Health 23(1), 630 (2023). (PMID: 3766728610476426)
Huang, D. et al. Optical coherence tomography. Science 254(5035), 1178–1181 (1991). (PMID: 19571694638169)
Podoleanu, A. G. & Bradu, A. Master-slave interferometry for parallel spectral domain interferometry sensing and versatile 3D optical coherence tomography. Opt. Exp. 21(16), 19324–19338 (2013).
Hwang, S.-Y., Choi, E.-S., Kim, Y.-S., Gim, B.-E., Ha, M., & Kim, H.-Y. Health effects from exposure to dental diagnostic X-ray. Environ. Health Toxicol. 33(4) (2018).
Kim, H. J., Kim, H. N., Raza, H. S., Park, H. B. & Cho, S. O. An intraoral miniature X-ray tube based on carbon nanotubes for dental radiography. Nucl. Eng. Technol. 48(3), 799–804 (2016).
Chauhan, V. & Wilkins, R. C. A comprehensive review of the literature on the biological effects from dental X-ray exposures. Int. J. Radiat. Biol. 95(2), 107–119 (2019). (PMID: 30496029)
Asahara, T. et al. Helpfulness of effective atomic number image in forensic dental identification: Photon-counting computed tomography is suitable. Comput. Biol. Med. 184, 109333 (2025). (PMID: 39522368)
Ashame, L. A., Youssef, S. M., Elagamy, M. N., Othman, A. & El-Sheikh, S. M. A computer-aided model for dental image diagnosis utilizing convolutional neural networks. J. Adv. Res. Appl. Sci. Eng. Technol. 52(2), 15–25 (2025).
Mahanty, M., Kumar, P.H., Sushma, M., Chand, I.T., Abhishek, K. & Chowdary, C.S.R. A comparative study on construction of 3D objects from 2D images. In Smart Technologies in Data Science and Communication: Proceedings of SMART-DSC 2021. 205–222. (Springer, 2021).
Matthews, I., Xiao, J. & Baker, S. 2D vs. 3D deformable face models: Representational power, construction, and real-time fitting. Int. J. Comput. Vis. 75, 93–113 (2007).
Duta, N., Jain, A. K. & Dubuisson-Jolly, M.-P. Automatic construction of 2D shape models. IEEE Trans. Pattern Anal. Mach. Intell. 23(5), 433–446 (2001).
Karako, K., Wu, Q. & Gao, J. Three-dimensional imaging technology offers promise in medicine. Drug Discov. Ther. 8(2), 96–97 (2014). (PMID: 24815585)
Duhn, C., Thalji, G., Al-Tarwaneh, S. & Cooper, L. F. A digital approach to robust and esthetic implant overdenture construction. J. Esthetic Restor. Dent. 33(1), 118–126 (2021).
Rougée, A., Picard, C., Ponchut, C. & Trousset, Y. Geometrical calibration of X-ray imaging chains for three-dimensional reconstruction. Comput. Med. Imaging Graph. 17(4–5), 295–300 (1993). (PMID: 8306301)
Mu, Y., Zuo, X., Guo, C., Wang, Y., Lu, J., Wu, X., Xu, S., Dai, P., Yan, Y. & Cheng, L. GSD: View-guided Gaussian splatting diffusion for 3D reconstruction. In European Conference on Computer Vision. 55–72 (Springer, 2025).
Zhou, W., Shi, X., She, Y., Liu, K. & Zhang, Y. Semi-supervised single-view 3D reconstruction via multi shape prior fusion strategy and self-attention. Comput. Graph. 126, 104142 (2025).
Liu, A., Lin, C., Liu, Y., Long, X., Dou, Z., Guo, H.-X., Luo, P. & Wang, W. Part123: Part-aware 3D reconstruction from a single-view image. In ACM SIGGRAPH 2024 Conference Papers. 1–12 (2024).
Verma, P. & Srivastava, R. Three stage deep network for 3D human pose reconstruction by exploiting spatial and temporal data via its 2D pose. J. Vis. Commun. Image Represent. 71, 102866 (2020).
Verma, P. & Srivastava, R. Two-stage multi-view deep network for 3D human pose reconstruction using images and its 2D joint heatmaps through enhanced stack-hourglass approach. Vis. Comput. 38(7), 2417–2430 (2022).
Zhu, Z. & Li, G. Construction of 3D human distal femoral surface models using a 3D statistical deformable model. J. Biomech. 44(13), 2362–2368 (2011). (PMID: 217831953156365)
Olveres, J. et al. What is new in computer vision and artificial intelligence in medical image analysis applications. Quant. Imaging Med. Surg. 11(8), 3830 (2021). (PMID: 343417538245941)
Plooij, J. M. et al. Digital three-dimensional image fusion processes for planning and evaluating orthodontics and orthognathic surgery. A systematic review. Int. J. Oral Maxillofac. Surg. 40(4), 341–352 (2011). (PMID: 21095103)
Brosky, M., Major, R., DeLong, R. & Hodges, J. Evaluation of dental arch reproduction using three-dimensional optical digitization. J. Prosthet. Dent. 90(5), 434–440 (2003). (PMID: 14586306)
Aragón, M. L., Pontes, L. F., Bichara, L. M., Flores-Mir, C. & Normando, D. Validity and reliability of intraoral scanners compared to conventional gypsum models measurements: A systematic review. Eur. J. Orthodont. 38(4), 429–434 (2016).
Xu, Y., Tong, X. & Stilla, U. Voxel-based representation of 3D point clouds: Methods, applications, and its potential use in the construction industry. Autom. Construct. 126, 103675 (2021).
Yu, D., Ji, S., Liu, J. & Wei, S. Automatic 3D building reconstruction from multi-view aerial images with deep learning. ISPRS J. Photogramm. Remote Sens. 171, 155–170 (2021).
Mirzaei, K. et al. 3D point cloud data processing with machine learning for construction and infrastructure applications: A comprehensive review. Adv. Eng. Inform. 51, 101501 (2022).
Gao, J. et al. Get3D: A generative model of high quality 3D textured shapes learned from images. Adv. Neural Inf. Process. Syst. 35, 31841–31854 (2022).
Wang, D., Cui, X., Chen, X., Zou, Z., Shi, T., Salcudean, S., Wang, Z.J. & Ward, R. Multi-view 3D reconstruction with transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5722–5731 (2021).
Liang, Y., Song, W., Yang, J., Qiu, L., Wang, K. & He, L. X2teeth: 3D teeth reconstruction from a single panoramic radiograph. In International Conference on Medical Image Computing and Computer-Assisted Intervention (2020).
Chen, Y., Gao, S., Tu, P. & Chen, X. Automatic 3D teeth reconstruction from five intra-oral photos using parametric teeth model. In IEEE Transactions on Visualization and Computer Graphics (2023).
Ali, F.I. & Al-dahan, Z.T. Teeth model reconstruction based on multiple view image capture. In IOP Conference Series: Materials Science and Engineering. Vol. 978. 012009 . (IOP Publishing, 2020).
Farook, T. H. et al. Computer-aided design and 3-dimensional artificial/convolutional neural network for digital partial dental crown synthesis and validation. Sci. Rep. 13(1), 1561 (2023). (PMID: 367093809884213)
Minhas, S. et al. Artificial intelligence for 3D reconstruction from 2D panoramic X-rays to assess maxillary impacted canines. Diagnostics 14(2), 196 (2024). (PMID: 3824807210813869)
Song, W., Zheng, H., Yang, J., Liang, C. & He, L. Oral-nexf: 3D oral reconstruction with neural x-ray field from panoramic imaging. arXiv preprint arXiv:2303.12123 (2023).
Li, X., Meng, M., Huang, Z., Bi, L., Delamare, E., Feng, D., Sheng, B. & Kim, J. 3Dpx: Progressive 2D-to-3D oral image reconstruction with hybrid mlp-cnn networks. arXiv preprint arXiv:2408.01292 (2024).
Ma, W., Wu, H., Xiao, Z., Feng, Y., Wu, J. & Liu, Z. Px2tooth: Reconstructing the 3D point cloud teeth from a single panoramic x-ray. In International Conference on Medical Image Computing and Computer-Assisted Intervention. 411–421 (Springer, 2024).
Choy, C.B., Xu, D., Gwak, J., Chen, K. & Savarese, S. 3D-r2n2: A unified approach for single and multi-view 3D object reconstruction. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VIII 14. 628–644 (Springer, 2016).
Zhao, W., Yang, C., Ye, J., Zhang, R., Yan, Y., Yang, X., Dong, B., Hussain, A. & Huang, K. From 2D images to 3D model: weakly supervised multi-view face reconstruction with deep fusion. arXiv preprint arXiv:2204.03842 (2022).
Tan, M. & Le, Q. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning. 6105–6114 (PMLR, 2019).
Tadepalli, Y., Kollati, M., Kuraparthi, S. & Kora, P. Efficientnet-b0 based monocular dense-depth map estimation. Trait. Signal 38(5) (2021).
Mohamed, W. TeethNet Dataset. https://github.com/waleedmm/TeethNet-Dataset/tree/main . Accessed Jan 2025 (2025).
Jecklin, S., Shen, Y., Gout, A., Suter, D., Calvet, L., Zingg, L., Straub, J., Cavalcanti, N.A., Farshad, M. & Fürnstahl, P. et al. Domain adaptation strategies for 3D reconstruction of the lumbar spine using real fluoroscopy data. arXiv preprint arXiv:2401.16027 (2024).
Willmott, C. J. & Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30(1), 79–82 (2005).
Köksoy, O. Multiresponse robust design: Mean square error (MSE) criterion. Appl. Math. Comput. 175(2), 1716–1729 (2006).
Schluchter, M.D. Mean square error. Encycl. Biostat. 5 (2005).
Chai, T. & Draxler, R. R. Root mean square error (RMSE) or mean absolute error (MAE). Geosci. Model Dev. Discuss. 7(1), 1525–1534 (2014).
Ou, X. et al. Moving object detection method via resnet-18 with encoder–decoder structure in complex scenes. IEEE Access 7, 108152–108160 (2019).
Karen, S. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556 (2014).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 770–778 (2016).
Kalitsios, G., Konstantinidis, D., Daras, P. & Dimitropoulos, K. Dynamic grouping with multi-manifold attention for multi-view 3D object reconstruction. IEEE Access (2024).
Liu, C., Zhu, M., Chen, Y., Wei, X. & Li, H. Paprec: 3D point cloud reconstruction based on prior-guided adaptive probabilistic network. Sensors 25(5), 1354 (2025). (PMID: 4009612411902572)
Yang, S., Xu, M., Xie, H., Perry, S. & Xia, J. Single-view 3D object reconstruction from shape priors in memory. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3152–3161 (2021).
Jebara, T., Azarbayejani, A. & Pentland, A. 3D structure from 2D motion. IEEE Signal Process. Mag. 16(3), 66–84 (1999).
Usta, U. Y. Comparison of quaternion and Euler angle methods for joint angle animation of human figure models (Naval Postgraduate School, 1999).
Nadimpalli, K.V., Chattopadhyay, A. & Rieck, B. Euler characteristic transform based topological loss for reconstructing 3D images from single 2D slices. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 571–579 (2023).
Liu, C., Xu, J. & Wang, F. A review of keypoints’ detection and feature description in image registration. Sci. Program. Wiley Online Lib. 2021(1), 8509164 (2021).
Hassaballah, M. & Awad, A.I. Detection and description of image features: An introduction. In Image Feature Detectors and Descriptors: Foundations and Applications. 1–8 (Springer, 2016).
Jakubović, A. & Velagić, J. Image feature matching and object detection using brute-force matchers. In 2018 International Symposium ELMAR. 83–86 (IEEE, 2018).
Antony, N. & Devassy, B.R. Implementation of image/video copy-move forgery detection using brute-force matching. In 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI). 1085–1090 (IEEE, 2018).
Liu, Y., Zhang, H., Guo, H. & Xiong, N. N. A fast-brisk feature detector with depth information. Sensors 18(11), 3908 (2018). (PMID: 304285806263410)
Leutenegger, S., Chli, M. & Siegwart, R.Y. Brisk: Binary robust invariant scalable keypoints. In 2011 International Conference on Computer Vision. 2548–2555 (IEEE, 2011).
Hartley, R. I. & Sturm, P. Triangulation. Comput. Vis. Image Underst. 68(2), 146–157 (1997).
Wei, D. et al. Clustering, triangulation, and evaluation of 3D lines in multiple images. ISPRS J. Photogramm. Remote Sens. 218, 678–692 (2024).
Vedaldi, A. An open implementation of the sift detector and descriptor. (UCLA CSD, 2007).
Mortensen, E.N., Deng, H. & Shapiro, L. A sift descriptor with global context. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05). Vol. 1. 184–190 (IEEE, 2005).
Rublee, E., Rabaud, V., Konolige, K. & Bradski, G. Orb: An efficient alternative to sift or surf. In 2011 International Conference on Computer Vision. 2564–2571 (IEEE, 2011).
Bansal, M., Kumar, M. & Kumar, M. 2D object recognition: A comparative analysis of sift, surf and orb feature descriptors. Multimed. Tools Appl. 80(12), 18839–18857 (2021).
Calonder, M. et al. Brief: Computing a local binary descriptor very fast. IEEE Trans. Pattern Anal. Mach. Intell. 34(7), 1281–1298 (2011). (PMID: 22084141)
Calonder, M., Lepetit, V., Strecha, C. & Fua, P. Brief: Binary robust independent elementary features. In Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part IV. Vol. 11. 778–792 (Springer, 2010).
Sharma, S. K. & Jain, K. Image stitching using Akaze features. J. Indian Soc. Remote Sens. 48(10), 1389–1401 (2020).
Pieropan, A., Björkman, M., Bergström, N. & Kragic, D. Feature descriptors for tracking by detection: A benchmark. arXiv preprint arXiv:1607.06178 (2016).
Weitere Informationen
Dental diseases are the primary cause of oral health concerns around the world, affecting millions of people. Therefore, recent developments in imaging technologies have transformed the detection and treatment of oral problems. Applying three-dimensional (3D) reconstruction from two-dimensional (2D) dental images, such as X-rays, is a potential development field. 3D reconstruction technology converts real-world goals into mathematical models that are compatible with computer logic expressions. It's been commonly used in dentistry. Particularly for patients with a vomiting reflex, 3D imaging techniques minimize patient discomfort and shorten the length of the examination or treatment. Therefore, this research paper proposes a new 3D reconstruction model from 2D multi-view dental images. The proposed framework consists of three stages. The first stage is the encoder stage, which extracts meaningful features from the 2D images. The second stage captures spatial and semantic information essential for the reconstruction task. The third stage is recurrence, which uses 3D long short-term memory (LSTM). It ensures that the information from various viewpoints is effectively integrated to produce a coherent representation of the 3D structure and decoder stage to translate the aggregated features from the LSTM into a fully reconstructed 3D model. When the proposed model was tested on the ShapeNet dataset, the suggested model achieved a maximum intersection over union (IoU) of 89.98% and an F1_score of 94.11%. A special case of 3D reconstruction, a dental dataset, has been created with the same structure as the ShapeNet dataset to evaluate our system. The proposed approach's results show promising accomplishments compared to many state-of-the-art approaches, and they motivate the authors to make plans for further improvement.
(© 2025. The Author(s).)
Declarations. Competing interests: The authors declare no competing interests.