Enlarge the Error Prediction Dataset in 3-D Printing: An Unsupervised Dental Crown Mesh Generator

作     者：Zhao, Meihua Xiong, Gang Fang, Qihang Dong, Xisong Wang, Fang Han, Yunjun Shen, Zhen Wang, Fei-Yue 

作者机构：Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat State Key Lab Multimodal Artificial Intelligence Beijing 100190 Peoples R China Chinese Acad Sci Guangdong Engn Res Ctr 3D Printing & Intelligent Cloud Comp Ctr Dongguan 523808 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS》 (IEEE Trans. Computat. Soc. Syst.)

年 卷 期：2024年第11卷第6期

页      面：7929-7940页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Key Research and Development Program of China [2021YFB3301504] National Natural Science Foundation of China [92267103, 92360307] Guangdong Basic and Applied Basic Research Foundation [2021B1515140034] Beijing Natural Science Foundation [L233005] 

主　　题：3-D printing depth image refinement (DR) displacement map (DM) generative adversarial network (GAN) mesh refinement 

摘      要：The quality of the dataset is critical to the performance of neural networks for error prediction in 3-D printing. In order to enlarge the dataset, we propose a customized two-stage framework, cascaded cross-modality generative adversarial networks (CCMGANs), for generating dental crown meshes in an unsupervised manner. At the first stage, a displacement map-guided generative adversarial network (GAN) is used to generate coarse meshes with diverse shapes. At the second stage, fine-grained details are added to the coarse meshes using an image-based GAN. Unlike previous work that integrates a differentiable renderer into the mesh deformation process directly, we adopt a two-step strategy. First, we use a depth image refinement module to achieve the domain transformation from the rendered depth images of the generated meshes to those of the real ones. Then, we propose a mesh refinement module to optimize the coarse meshes in an image-supervised manner. To alleviate the self-intersection problem, we propose a loss to penalize the distances of point pairs in self-intersection regions. Experimental results show that our method is able to generate highly realistic meshes and outperforms the state-of-the-art point cloud generation method TreeGCN in terms of the metrics FDD, MMD-CD, MMD-EMD, and COV-EMD. Furthermore, we utilize the generated data to augment the original dataset, and demonstrate that the generated data can effectively improve the accuracy of the error prediction task in 3-D printing.