Foldable chain-based transformation method of 3D models

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Complex & Intelligent Systems Pub Date : 2023-12-19 DOI:10.1007/s40747-023-01302-1
Yuxiao Zhang, Jin Wang, Dongliang Zhang, Guodong Lu
{"title":"Foldable chain-based transformation method of 3D models","authors":"Yuxiao Zhang, Jin Wang, Dongliang Zhang, Guodong Lu","doi":"10.1007/s40747-023-01302-1","DOIUrl":null,"url":null,"abstract":"<p>A 3D transformable model can be transformed into different shapes through folding operations to suit different needs, such as a table or a chair in daily life. Furthermore, the features of foldable structure and flat components allow it to be folded into a smaller stack for compact storage when not in use. To this end, this study applies a new foldable modular chain structure and proposes a novel method of constructing 3D models into 3D shapes based on this structure and guiding the transformation between shapes. For the construction of the model, that is, to find a module chain path that constructs the model shape, the divide-and-conquer method is adopted. The model is first divided into multiple units, and then the search for the linearly connected module sub-path is executed for each unit. This involves three major steps: unit-based segmentation of the model, search for the unit tree structure that can form the target 3D shape, and search for the modular chain path based on the unit tree. The experimental cases demonstrate the application of the square modular chain in the fields of furniture and toys and prove the effectiveness of the method in constructing and transforming the foldable chain-type modular configurations of the input 3D models.</p>","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"235 1","pages":""},"PeriodicalIF":5.0000,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Complex & Intelligent Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s40747-023-01302-1","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

A 3D transformable model can be transformed into different shapes through folding operations to suit different needs, such as a table or a chair in daily life. Furthermore, the features of foldable structure and flat components allow it to be folded into a smaller stack for compact storage when not in use. To this end, this study applies a new foldable modular chain structure and proposes a novel method of constructing 3D models into 3D shapes based on this structure and guiding the transformation between shapes. For the construction of the model, that is, to find a module chain path that constructs the model shape, the divide-and-conquer method is adopted. The model is first divided into multiple units, and then the search for the linearly connected module sub-path is executed for each unit. This involves three major steps: unit-based segmentation of the model, search for the unit tree structure that can form the target 3D shape, and search for the modular chain path based on the unit tree. The experimental cases demonstrate the application of the square modular chain in the fields of furniture and toys and prove the effectiveness of the method in constructing and transforming the foldable chain-type modular configurations of the input 3D models.

Abstract Image

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
基于折叠链的三维模型变换方法
三维可变形模型可以通过折叠操作变成不同的形状,以适应不同的需要,例如日常生活中的桌子或椅子。此外,可折叠结构和扁平组件的特点使其在不使用时可折叠成较小的堆叠,以便紧凑存放。为此,本研究应用了一种新的可折叠模块链结构,并提出了一种基于该结构将三维模型构建为三维形状并引导形状之间转换的新方法。在构建模型时,即寻找构建模型形状的模块链路径时,采用了分而治之的方法。首先将模型划分为多个单元,然后针对每个单元搜索线性连接的模块子路径。这包括三个主要步骤:基于单元对模型进行分割,搜索能形成目标三维形状的单元树结构,以及搜索基于单元树的模块链路径。实验案例展示了方形模块链在家具和玩具领域的应用,证明了该方法在构建和转换输入三维模型的可折叠链式模块配置方面的有效性。
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
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
期刊最新文献
FL-Joint: joint aligning features and labels in federated learning for data heterogeneity Large-scale multiobjective competitive swarm optimizer algorithm based on regional multidirectional search Towards fairness-aware multi-objective optimization Low-frequency spectral graph convolution networks with one-hop connections information for personalized tag recommendation A decentralized feedback-based consensus model considering the consistency maintenance and readability of probabilistic linguistic preference relations for large-scale group decision-making
×
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