ToothInpaintor: Tooth Inpainting from Partial 3D Dental Model and 2D Panoramic Image
This work addresses a domain-specific problem in dentistry by enabling full tooth model reconstruction without high-radiation CBCT scans, offering a practical solution for orthodontic planning.
The paper tackles the problem of reconstructing full 3D tooth models, including roots, from partial 3D scans and 2D panoramic images, which is crucial for orthodontic treatment planning but limited by radiation concerns in CBCT imaging. The proposed ToothInpaintor method achieves accurate and robust reconstruction, outperforming state-of-the-art techniques on a real-world clinical dataset.
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.