Root Completion from Intraoral Scans of Tooth Crowns using Diffusion with Patch Perturbation

Intraoral scan (IoS) provides high-resolution data on the tooth crown, but does not contain information on the tooth root and thus has limitations in applications requiring 3D models of the whole tooth, e.g., virtual dental simulators. In this paper, we consider a diffusion-based model for root completion from IoS crowns. A key challenge is the lack of ground truth, i.e., the scan data of roots are typically unavailable. To train our model, we instead use the Cone-Beam CT (CBCT) data matched to IoS images, and use its crown as input and root as the pseudo-ground truth. Due to the difference in input data between training (CBCT crown) and inference (IoS crown), there is an issue of domain shift. To address the issue, we take a coarse-to-fine approach: we make a coarse prediction of roots using Transformer encoder; introduce Perturbed Patch Generator (PPG) which generates patches from coarse points and perturbs them with noise for a robust prediction against the domain shift; and use Transformer denoiser for refined reconstruction. We also propose loss functions designed to facilitate the training of the denoiser with perturbed patches. Experiments show that our method outperforms prior techniques in various benchmark evaluations, demonstrating its robust performance in generating high-quality root data. The source code will be publicly released upon acceptance.