DCrownFormer⁺: Morphology-aware mesh generation and refinement transformer for dental crown prosthesis from 3D scan...

Dental prostheses are 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, this procedure is time-consuming because the initial tooth template for designing dental crowns is not personalized. In this paper, we propose a novel end-to-end morphology-aware mesh generation and refinement transformer called DCrownFormer⁺ to directly and efficiently generate high-fidelity and realistic meshes for dental crowns from the mesh inputs of 3D scans of preparation and antagonist teeth. DCrownFormer⁺ captures local and global geometric features from mesh inputs using a geometric feature attention descriptor and the transformer encoder. We leverage a morphology-aware cross-attention module with curvature-penalty Chamfer distance loss (CPL) to generate the points and normals of a dental crown from geometric features at the transformer decoder. Then, a coarse indicator grid is directly estimated from the generated points and normals of the dental crown using differentiable Poisson surface reconstruction. To further improve the fine details of the occlusal surfaces, we propose a learning-based refinement method called implicit grid refinement network with a gradient-penalty mesh reconstruction loss (GPL) to generate high-fidelity and realistic dental crown meshes by refining the details of the coarse indicator grid. Our experimental results demonstrate that DCrownFormer⁺ is superior to other methods in improving the shape completeness, surface smoothness, and morphological details of occlusal surfaces, such as dental grooves and cusps. We further validate the effectiveness of key components and the significant benefits of CPL and GPL through ablation studies.