Geometry-Based Layout Generation with Hyper-Relations AMONG Objects

IF 2.5 4区 计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Graphical Models Pub Date : 2021-07-01 DOI:10.1016/j.gmod.2021.101104
Shao-Kui Zhang , Wei-Yu Xie , Song-Hai Zhang
{"title":"Geometry-Based Layout Generation with Hyper-Relations AMONG Objects","authors":"Shao-Kui Zhang ,&nbsp;Wei-Yu Xie ,&nbsp;Song-Hai Zhang","doi":"10.1016/j.gmod.2021.101104","DOIUrl":null,"url":null,"abstract":"<div><p><span>Recent studies show increasing demands and interests in automatic layout generation, while there is still much room for improving the plausibility<span> and robustness. In this paper, we present a data-driven layout generation framework without model formulation and loss term optimization. We achieve and organize priors directly based on samples from datasets instead of sampling probabilistic distributions. Therefore, our method enables expressing relations among three or more objects that are hard to be mathematically modeled. Subsequently, a non-learning geometric algorithm is proposed to arrange objects considering constraints such as positions of walls and windows. Experiments show that the proposed method outperforms the state-of-the-art and our generated layouts are competitive to those designed by professionals.</span></span><span><sup>1</sup></span></p></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"116 ","pages":"Article 101104"},"PeriodicalIF":2.5000,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.gmod.2021.101104","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Graphical Models","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1524070321000096","RegionNum":4,"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: 8

Abstract

Recent studies show increasing demands and interests in automatic layout generation, while there is still much room for improving the plausibility and robustness. In this paper, we present a data-driven layout generation framework without model formulation and loss term optimization. We achieve and organize priors directly based on samples from datasets instead of sampling probabilistic distributions. Therefore, our method enables expressing relations among three or more objects that are hard to be mathematically modeled. Subsequently, a non-learning geometric algorithm is proposed to arrange objects considering constraints such as positions of walls and windows. Experiments show that the proposed method outperforms the state-of-the-art and our generated layouts are competitive to those designed by professionals.1

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
具有对象间超关系的基于几何的布局生成
近年来的研究表明,自动布局生成的需求和兴趣越来越大,但其可行性和鲁棒性仍有很大的提高空间。在本文中,我们提出了一个数据驱动的布局生成框架,该框架不需要模型制定和损失项优化。我们直接基于数据集的样本而不是抽样概率分布来实现和组织先验。因此,我们的方法可以表达三个或更多对象之间的关系,这些对象很难用数学建模。在此基础上,提出了一种考虑墙体、窗户位置等约束条件的非学习几何算法。实验表明,所提出的方法优于目前最先进的方法,并且我们生成的布局与专业人员设计的布局相比具有竞争力
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
Graphical Models
Graphical Models 工程技术-计算机:软件工程
CiteScore
3.60
自引率
5.90%
发文量
15
审稿时长
47 days
期刊介绍: Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.
期刊最新文献
HammingVis: A visual analytics approach for understanding erroneous outcomes of quantum computing in hamming space A detail-preserving method for medial mesh computation in triangular meshes Exploring the neural landscape: Visual analytics of neuron activation in large language models with NeuronautLLM GarTemFormer: Temporal transformer-based for optimizing virtual garment animation Building semantic segmentation from large-scale point clouds via primitive recognition
×
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