Abstract
Deep neural networks have made a number of achievements both in tooth segmentation and arrangement on complete 3D dental models. But few studies have used deep learning methods on the tooth completion and reconstruction on the incomplete dental models. To rebuild the missing tooth from incomplete dental model, we propose a two-stage approach ToothCR which takes advantage of the powerful learning capabilities of deep neural networks. In the first stage, ToothCR introduces a geometry-aware transformer encoder into the 3D dental model completion task. Self-attention mechanism in transformers could better model long-range dependencies in point cloud and ensure the predicted missing parts to have precise geometric structures. In the second stage, ToothCR uses a novel surface reconstruction algorithm to recover the surface of the predicted missing tooth. The reconstruction algorithm guarantees the generated surface to be watertight and avoids holes or redundant meshes which traditional methods may produce. Extensive experiments conducted on 3D dental datasets show that our approach outperforms state-of-the-art methods both in qualitative and quantitative results.
This work was supported by National Key Research and Development Program of China (2019YFB1706900), and the Fundamental Research Funds for the Central Universities (30920021131).
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Zhu, H., Jia, X., Zhang, C., Liu, T. (2022). 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. Lecture Notes in Computer Science(), vol 13282. Springer, Cham. https://doi.org/10.1007/978-3-031-05981-0_13
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