MVDC : A Multi-view Dental Completion Model Based on Contrastive Learning
Xunyu Yang, Qingxin Deng, Minghan Huang, Landu Jiang, Dian Zhang
Abstract
Restoring the patient’s occlusal function of broken teeth is a challenging task since tooth texture is very complex, a slight deviation may affect the patient’s chewing function and temporomandibular joint function. Therefore, how to efficiently repair the complete shape and real surface of the crown is a critical problem. Traditional technologies are hard to restore complete shape of the dental crown or lack inlay surface details, due to dataset limitations and complexity of missing parts. In this paper, we propose a multi-view crown restoration framework MVDC based on contrastive learning. Specifically, MVDC contains: 1) a multi-view generator with a specially designed loss measurement by using contrastive learning; 2) a multi-scale discriminator mechanism able to consider relation and consistency between teeth from different scales; 3) an occlusal groove extraction network to extract the occlusal details. We conducted extensive experiments on existing public datasets. The results showcase the superior performance of MVDC.
BibTeX
@inproceedings{icassp2025_mvdcamultiviewde,
title = {MVDC : A Multi-view Dental Completion Model Based on Contrastive Learning},
author = {Xunyu Yang and Qingxin Deng and Minghan Huang and Landu Jiang and Dian Zhang},
booktitle = {ICASSP 2025},
year = {2025}
}