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

文献类型：期刊论文

作者Zhao, Meihua1,2; Xiong, Gang3,4; Fang, Qihang1,2; Dong, Xisong3,4; Wang, Fang1,2; Han, Yunjun5; Shen, Zhen3,4; Wang, Fei-Yue
刊名IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
出版日期2024-07-18
页码12
关键词Shape Generative adversarial networks Printing Point cloud compression Dentistry Task analysis Deformation 3-D printing depth image refinement (DR) displacement map (DM) generative adversarial network (GAN) mesh refinement
ISSN号2329-924X
DOI10.1109/TCSS.2024.3417388
通讯作者Shen, Zhen(zhen.shen@ia.ac.cn)
英文摘要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.
WOS关键词DESIGN
资助项目National Key Research and Development Program of China[2021YFB3301504] ; National Natural Science Foundation of China[92267103] ; National Natural Science Foundation of China[92360307] ; Guangdong Basic and Applied Basic Research Foundation[2021B1515140034] ; Beijing Natural Science Foundation[L233005]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001273034900001
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China ; Guangdong Basic and Applied Basic Research Foundation ; Beijing Natural Science Foundation
源URL[http://ir.ia.ac.cn/handle/173211/59330]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Shen, Zhen
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Cloud Comp Ctr, Guangdong Engn Res Ctr 3-D Printing & Intelligent, Dongguan 523808, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Meihua,Xiong, Gang,Fang, Qihang,et al. Enlarge the Error Prediction Dataset in 3-D Printing: An Unsupervised Dental Crown Mesh Generator[J]. IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,2024:12.
APA Zhao, Meihua.,Xiong, Gang.,Fang, Qihang.,Dong, Xisong.,Wang, Fang.,...&Wang, Fei-Yue.(2024).Enlarge the Error Prediction Dataset in 3-D Printing: An Unsupervised Dental Crown Mesh Generator.IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS,12.
MLA Zhao, Meihua,et al."Enlarge the Error Prediction Dataset in 3-D Printing: An Unsupervised Dental Crown Mesh Generator".IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS (2024):12.

入库方式： OAI收割

来源：自动化研究所

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