bibtext: @InProceedings{ Yan_DCrownFormer_MICCAI2024,
                   author = { Yang, Su and Han, Jiyong and Lim, Sang-Heon and Yoo, Ji-Yong and Kim, SuJeong and Song, Dahyun and Kim, Sunjung and Kim, Jun-Min and Yi, Won-Jin },
                   title = { { DCrownFormer: Morphology-aware Point-to-Mesh Generation Transformer for Dental Crown Prosthesis from 3D Scan Data of Antagonist and Preparation Teeth } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15006 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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/.
title: DCrownFormer: Morphology-aware Point-to-Mesh Generation Transformer for Dental Crown Prosthesis from 3D Scan Data of Antagonist and Preparation Teeth
authors: Yang, Su Han, Jiyong Lim, Sang-Heon Yoo, Ji-Yong Kim, SuJeong Song, Dahyun Kim, Sunjung Kim, Jun-Min Yi, Won-Jin