TriClsNet: Surface Reconstruction via Graph-based Triangle Classification

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Computer-Aided Design Pub Date : 2024-05-21 DOI:10.1016/j.cad.2024.103729
Fei Liu, Ying Pan, Qingguang Li
{"title":"TriClsNet: Surface Reconstruction via Graph-based Triangle Classification","authors":"Fei Liu,&nbsp;Ying Pan,&nbsp;Qingguang Li","doi":"10.1016/j.cad.2024.103729","DOIUrl":null,"url":null,"abstract":"<div><p>In this paper, we introduce TriClsNet, a novel learning-based network that reconstructs surfaces by reframing the triangle classification problem as a graph node classification problem. An improved graph-based triangle classification module is employed to aggregate information from neighboring triangles, effectively leveraging local neighborhood information and enhancing triangle classification accuracy. Additionally, a self-supervised learning branch is incorporated to predict point cloud normals, aiding our network in better learning local point cloud features. Furthermore, a new loss function is designed to guide our network in effective multi-task learning, encompassing both graph node classification and normal prediction. Comparative experimental results on ShapeNet demonstrate that our method can efficiently perform surface reconstruction, outperforming existing methods in the aspects of preserving surface details, reducing holes, and generalization.</p></div>","PeriodicalId":50632,"journal":{"name":"Computer-Aided Design","volume":"173 ","pages":"Article 103729"},"PeriodicalIF":3.0000,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer-Aided Design","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010448524000563","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, SOFTWARE ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, we introduce TriClsNet, a novel learning-based network that reconstructs surfaces by reframing the triangle classification problem as a graph node classification problem. An improved graph-based triangle classification module is employed to aggregate information from neighboring triangles, effectively leveraging local neighborhood information and enhancing triangle classification accuracy. Additionally, a self-supervised learning branch is incorporated to predict point cloud normals, aiding our network in better learning local point cloud features. Furthermore, a new loss function is designed to guide our network in effective multi-task learning, encompassing both graph node classification and normal prediction. Comparative experimental results on ShapeNet demonstrate that our method can efficiently perform surface reconstruction, outperforming existing methods in the aspects of preserving surface details, reducing holes, and generalization.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
TriClsNet:通过基于图形的三角形分类进行曲面重构
本文介绍了基于学习的新型网络 TriClsNet,它通过将三角形分类问题重构为图节点分类问题来重构曲面。我们采用了改进的基于图的三角形分类模块来聚合邻近三角形的信息,从而有效利用本地邻域信息,提高三角形分类的准确性。此外,还加入了一个自监督学习分支来预测点云法线,帮助我们的网络更好地学习本地点云特征。此外,我们还设计了一个新的损失函数,以指导网络进行有效的多任务学习,包括图形节点分类和法线预测。在 ShapeNet 上的对比实验结果表明,我们的方法可以有效地进行曲面重建,在保留曲面细节、减少孔洞和泛化等方面都优于现有方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Computer-Aided Design
Computer-Aided Design 工程技术-计算机:软件工程
CiteScore
5.50
自引率
4.70%
发文量
117
审稿时长
4.2 months
期刊介绍: Computer-Aided Design is a leading international journal that provides academia and industry with key papers on research and developments in the application of computers to design. Computer-Aided Design invites papers reporting new research, as well as novel or particularly significant applications, within a wide range of topics, spanning all stages of design process from concept creation to manufacture and beyond.
期刊最新文献
Editorial Board Plate Manufacturing Constraint in Topology Optimization Using Anisotropic Filter Feature-aware Singularity Structure Optimization for Hex Mesh Fast algorithm for extracting domains and regions from three-dimensional triangular surface meshes Higher-degrees Hybrid Non-uniform Subdivision Surfaces
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1