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In orthodontic treatment, a full tooth model consisting of both the crown androot is indispensable in making the treatment plan. However, acquiring toothroot information to obtain the full tooth model from CBCT images is sometimesrestricted due to the massive radiation of CBCT scanning. Thus, reconstructingthe full tooth shape from the ready-to-use input, e.g., the partial intra-oralscan and the 2D panoramic image, is an applicable and valuable solution. Inthis paper, we propose a neural network, called ToothInpaintor, that takes asinput a partial 3D dental model and a 2D panoramic image and reconstructs thefull tooth model with high-quality root(s). Technically, we utilize theimplicit representation for both the 3D and 2D inputs, and learn a latent spaceof the full tooth shapes. At test time, given an input, we successfully projectit to the learned latent space via neural optimization to obtain the full toothmodel conditioned on the input. To help find the robust projection, a noveladversarial learning module is exploited in our pipeline. We extensivelyevaluate our method on a dataset collected from real-world clinics. Theevaluation, comparison, and comprehensive ablation studies demonstrate that ourapproach produces accurate complete tooth models robustly and outperforms thestate-of-the-art methods.

ToothInpaintor: Tooth Inpainting from Partial 3D Dental Model and 2D Panoramic Image