  In orthodontic treatment, a full tooth model consisting of both the crown and
root is indispensable in making the treatment plan. However, acquiring tooth
root information to obtain the full tooth model from CBCT images is sometimes
restricted due to the massive radiation of CBCT scanning. Thus, reconstructing
the full tooth shape from the ready-to-use input, e.g., the partial intra-oral
scan and the 2D panoramic image, is an applicable and valuable solution. In
this paper, we propose a neural network, called ToothInpaintor, that takes as
input a partial 3D dental model and a 2D panoramic image and reconstructs the
full tooth model with high-quality root(s). Technically, we utilize the
implicit representation for both the 3D and 2D inputs, and learn a latent space
of the full tooth shapes. At test time, given an input, we successfully project
it to the learned latent space via neural optimization to obtain the full tooth
model conditioned on the input. To help find the robust projection, a novel
adversarial learning module is exploited in our pipeline. We extensively
evaluate our method on a dataset collected from real-world clinics. The
evaluation, comparison, and comprehensive ablation studies demonstrate that our
approach produces accurate complete tooth models robustly and outperforms the
state-of-the-art methods.
