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From Mesh Completion to AI Designed Crown

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)


Designing a dental crown is a time-consuming and labor-intensive process. Our goal is to simplify crown design and minimize the tediousness of making manual adjustments while still ensuring the highest level of accuracy and consistency. To this end, we present a new end-to-end deep learning approach, coined Dental Mesh Completion (DMC), to generate a crown mesh conditioned on a point cloud context. The dental context includes the tooth prepared to receive a crown and its surroundings, namely the two adjacent teeth and the three closest teeth in the opposing jaw. We formulate crown generation in terms of completing this point cloud context. A feature extractor first converts the input point cloud into a set of feature vectors that represent local regions in the point cloud. The set of feature vectors is then fed into a transformer to predict a new set of feature vectors for the missing region (crown). Subsequently, a point reconstruction head, followed by a multi-layer perceptron, is used to predict a dense set of points with normals. Finally, a differentiable point-to-mesh layer serves to reconstruct the crown surface mesh. We compare our DMC method to a graph-based convolutional neural network which learns to deform a crown mesh from a generic crown shape to the target geometry. Extensive experiments on our dataset demonstrate the effectiveness of our method, which attains an average of 0.062 Chamfer Distance. The code is available at:

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  1. (2021)

  2. Hwang, J.-J., Azernikov , S., Efros, A.A., Yu, S.X.: Learning beyond human expertise with generative models for dental restorations (2018)

    Google Scholar 

  3. Yuan, F., et al.: Personalized design technique for the dental occlusal surface based on conditional generative adversarial networks. Int. J. Numer. Methods Biomed. Eng. 36(5), e3321 (2020)

    Google Scholar 

  4. Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A. : Image-to-image translation with conditional adversarial networks. In: CVPR (2017)

    Google Scholar 

  5. Lessard, O., Guibault, F., Keren, J., Cheriet, F.: Dental restoration using a multi-resolution deep learning approach. In: IEEE 19th International Symposium on Biomedical Imaging, (ISBI) (2022)

    Google Scholar 

  6. Zhu, H., Jia, X., Zhang, C., Liu, T.: ToothCR: a two-stage completion and reconstruction approach on 3D dental model. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds.) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. LNCS, vol. 13282. Springer, Cham (2022).

  7. Ping, Y., Wei, G., Yang, L., Cui, Z., Wang, W.: Self-attention implicit function networks for 3D dental data completion. Comput. Aided Geometr. Design 90, 102026 (2021)

    Google Scholar 

  8. Hosseinimanesh, G., et al. : Improving the quality of dental crown using a transformer-based method. Medical Imaging 2023: Physics of Medical Imaging, vol. 12463. SPIE (2023)

    Google Scholar 

  9. Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., Jiang, Y.-G.: Pixel2Mesh: generating 3D mesh models from single RGB images. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 55–71. Springer, Cham (2018).

    Chapter  Google Scholar 

  10. Huang, Z., Yu, Y., Xu, J., Ni, F., Le, X.: PF-Net: point fractal network for 3D point cloud completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7662–7670 (2020)

    Google Scholar 

  11. Yu, X., Rao, Y., Wang, Z., Liu, Z., Lu, J., Zhou, J.: PoinTr: diverse point cloud completion with geometry-aware transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12498–12507

    Google Scholar 

  12. Fei, B., Yang, W., Chen, W., Li, Z., et al.: Comprehensive review of deep learning-based 3D point clouds completion processing and analysis. arXiv Prepr. arXiv2203.03311 (2022)

    Google Scholar 

  13. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)

    Google Scholar 

  14. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: deep hierarchical feature learning on point sets in a metric space. Deep Hierarchical Feature Learning on Point Sets in a Metric Space (2017)

    Google Scholar 

  15. Yang, Y., Feng, C., Shen, Y., Tian, D.: FoldingNet: point cloud auto-encoder via deep grid deformation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 206–215 (2018)

    Google Scholar 

  16. Xiang, P., et al.: SnowflakeNet: point cloud completion by snowflake point deconvolution with skip-transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 5499–5509 (2021).

  17. Peng, S., Jiang, C., Liao, Y., Niemeyer, M., Pollefeys, M., Geiger, A.: Shape as points: a differentiable poisson solver. Journal, Advances in Neural Information Processing Systems (2021)

    Google Scholar 

  18. Litany, O., Bronstein, A., Bronstein, M., Makadia, A.: Deformable shape completion with graph convolutional autoencoders. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1886–1895 (2017)

    Google Scholar 

  19. Hui, K.-H., Li, R., Hu, J., Fu, C.-W.: Neural template: topology-aware reconstruction and disentangled generation of 3D meshes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18572–18582 (2022)

    Google Scholar 

  20. Yuan, W., Khot, T., Held, D., Mertz, C., Hebert, M.: PCN: point completion network. In: 3DV, pp. 728–737 (2018)

    Google Scholar 

  21. Alsheghri, A., et al. : Semi-supervised segmentation of tooth from 3D scanned dental arches. In: Medical Imaging, SPIE (2022)

    Google Scholar 

  22. Lorensen, W.E., Cline, H.E.: Marching cubes: a high-resolution 3D surface construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 87, pp. 163–169, New York, NY, USA, 1987. Association for Computing Machinery

    Google Scholar 

  23. Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. Journal, arXiv preprint arXiv:1711.05101 (2017)

  24. Tatarchenko, M., Richter, S.R., Ranftl, R., Li, Z., Koltun, V., Brox, T.: What do single-view 3D reconstruction networks learn? CoRR, abs/1905.03678 (2019)

    Google Scholar 

  25. Foti, S., et al.: Clarkson: intraoperative liver surface completion with graph convolutional VAE. In: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, pp. 198–207 (2020)

    Google Scholar 

  26. Sarkar, K., Varanasi, K., Stricker, D.: Learning quadrangulated patches for 3D shape parameterization and completion. In: International Conference on 3D Vision (3DV), pp. 383–392 (2017)

    Google Scholar 

  27. Kazhdan, M., Hoppe, H.: Johns Hopkins University: screened poisson surface reconstruction. ACM Trans. Graph. 32(3), 1–13 (2013)

    Google Scholar 

  28. Chen, X., et al.: Shape registration with learned deformations for 3D shape reconstruction from sparse and incomplete point clouds. J. Med. Image Anal. 74, 102228 (2021). Elsevier

    Google Scholar 

  29. Wang, Y., et al.: Dynamic Graph CNN for Learning on Point Clouds. ACM Trans. Graph. (TOG) 38, 1–12 (2019)

    Google Scholar 

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This work was funded by Kerenor Dental Studio, Intellident Dentaire Inc..

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Correspondence to Golriz Hosseinimanesh .

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Hosseinimanesh, G., Ghadiri, F., Guibault, F., Cheriet, F., Keren, J. (2023). From Mesh Completion to AI Designed Crown. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14228. Springer, Cham.

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