版权所有:内蒙古大学图书馆 技术提供:维普资讯• 智图
内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
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.