DCrownFormer: Morphology-Aware Point-to-Mesh Generation Transformer for Dental Crown Prosthesis from 3D Scan Data of Antagonist and Preparation Teeth

Dental prosthesis is important in designing artificial replacements to restore the function and appearance of teeth. However, designing a patient-specific dental prosthesis is still labor-intensive and depends on dental professionals with knowledge of oral anatomy and their experience. Also, the initial tooth template for designing dental crowns is not personalized. In this paper, we propose a novel point-to-mesh generation transformer (DCrownFormer) to directly and efficiently generate dental crown meshes from point inputs of 3D scans of antagonist and preparation teeth. Specifically, to learn morphological relationships between a point input and generated points of a dental crown, we introduce a morphology-aware cross-attention module (MCAM) in a transformer decoder and curvature-penalty loss (CPL). Furthermore, we adopt Differentiable Poisson surface reconstruction for mesh reconstruction from generated points and normals of a dental crown by directly optimizing an indicator function using mesh reconstruction loss (MRL). Experimental results demonstrate the superiority of DCrwonFormer compared with other methods, by improving morphological details of occlusal surfaces such as dental grooves and cusps. We further validate the effectiveness of MCAM, MRL, and significant benefits of CPL through ablation studies. The code is available at https://github.com/suyang93/DCrownFormer/ .