@article{Wang2025, 
author = {Chen Wang and Guangshun Wei and James Kit Hon Tsoi and Zhiming Cui and Shuyi Lu and Zhenpeng Liu and Yuanfeng Zhou},
title = {Diff-OSGN: Diffusion-based occlusal surface generation network with geometric constraints},
year = {2025},
journal = {Computational Visual Media},
volume = {11},
number = {4},
pages = {817-832},
keywords = {medical imaging, diffusion model, prosthodontics, functional occlusal surface design},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450498},
doi = {10.26599/CVM.2025.9450498},
abstract = {Designing a functional occlusal surface for denture crowns is a complex and important task in prosthodontics. Manual design is time-consuming and heavily relies on the dentist's experience, as it requires careful consideration of occlusal function. Due to the limitations of manual design, the field has turned to data-driven methods for occlusal surface design. However, many of these methods neglect critical geometric details, such as normals and curvature, impacting the quality of the occlusal surface. In this paper, we introduce Diff-OSGN, a novel denture crown occlusal surface generation network based on a denoising diffusion model, which focuses on generating the detailed geometric structure of denture crowns. We model the occlusal surface as a geometry map based on the occlusal plane, incorporating height and normal maps rasterized from intra-oral crown scanning. Both maps represent occlusal surface geometry, and their combination further enhances these details. Considering the crucial occlusal information, we extract features from the geometry maps of adjacent and occlusal teeth, using them as conditions in the reverse diffusion process to train our network for optimal occlusal function. Additionally, we define three geometric operators and corresponding loss functions as constraints to better extract geometric features of the target occlusal surface, such as ridges and grooves, for adequate supervision. Our results demonstrate that Diff-OSGN provides quantitatively and qualitatively superior performance than competing baselines and state-of-the-art methods.}
}