• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Sun Xiaopeng, Liu Shihan, Wang Zhenyan, Li Jiaojiao. Survey on Geometric Unfolding, Folding Algorithms and Applications[J]. Journal of Computer Research and Development, 2020, 57(11): 2389-2403. DOI: 10.7544/issn1000-1239.2020.20200126
Citation: Sun Xiaopeng, Liu Shihan, Wang Zhenyan, Li Jiaojiao. Survey on Geometric Unfolding, Folding Algorithms and Applications[J]. Journal of Computer Research and Development, 2020, 57(11): 2389-2403. DOI: 10.7544/issn1000-1239.2020.20200126

Survey on Geometric Unfolding, Folding Algorithms and Applications

Funds: This work was supported by the National Natural Science Foundation of China (61472170) and the Open Project of Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia (Beijing University of Posts and Telecommunications) (ITSM201301).
More Information
  • Published Date: October 31, 2020
  • Unfolding and folding problem is a popular research topic in computer graphics, and has a wide range of applications, such as industrial manufacturing, architectural design, medical treatment, and aviation technology. In this survey, we review the basic concepts of unfolding and folding problem, introduce the research and application in four fields: robot design, computer animation, deep learning and others. We discuss the research work of unfolding and folding problem in detail. First, according to the different degrees of unfolding, we summarize research progress and typical algorithm ideas from two aspects: full unfolding and approximate unfolding. Full unfolding is to unfold 3D objects into 2D space without overlapping and deformation. However, most objects cannot be directly unfolded, and only an approximately unfolded structure can be solved. Approximate unfolding is a non-overlapping and deformed process, which is unfolded into the plane domain by mapping. How to find the smallest deformation is the key to approximate unfolding. Second, according to the different folding forms, the folding problem is divided into two types: Origami and Kirigami. We divide Origami into rigid folding and curved folding according to the different forms of crease, such as straight crease and curved crease. Kirigami is a special folding method that combines cutting and folding technology, which drives folding by the elastic force or other external forces generated by cutting. Here, we mainly consider the technology or algorithm of using Kirigami technology to construct auxetic structures. In addition, in order to compare the advantages and disadvantages of the algorithm, we summarize the commonly used algorithm indicators of unfolding and folding algorithm. Then, we evaluate the typical algorithm in recent years, and analyze advantages and disadvantages. Finally, we summarize and propose the development trend of unfolding and folding, including algorithm accuracy and robustness, fold volumetric objects, self-driven process and intelligent application of Kirigami technology.
  • Related Articles

    [1]Tian Xuan, Xu Zezhou, Wang Zihan. Review of Deep Learning Based Query Suggestion[J]. Journal of Computer Research and Development, 2024, 61(12): 3168-3187. DOI: 10.7544/issn1000-1239.202220837
    [2]Lai Xinyu, Chen Si, Yan Yan, Wang Dahan, Zhu Shunzhi. Survey on Deep Learning Based Facial Attribute Recognition Methods[J]. Journal of Computer Research and Development, 2021, 58(12): 2760-2782. DOI: 10.7544/issn1000-1239.2021.20200870
    [3]Yu Ying, Zhu Huilin, Qian Jin, Pan Cheng, Miao Duoqian. Survey on Deep Learning Based Crowd Counting[J]. Journal of Computer Research and Development, 2021, 58(12): 2724-2747. DOI: 10.7544/issn1000-1239.2021.20200699
    [4]Pan Xuan, Xu Sihan, Cai Xiangrui, Wen Yanlong, Yuan Xiaojie. Survey on Deep Learning Based Natural Language Interface to Database[J]. Journal of Computer Research and Development, 2021, 58(9): 1925-1950. DOI: 10.7544/issn1000-1239.2021.20200209
    [5]Chen Bofeng, Li Jingdong, Lu Xingjian, Sha Chaofeng, Wang Xiaoling, Zhang Ji. Survey of Deep Learning Based Graph Anomaly Detection Methods[J]. Journal of Computer Research and Development, 2021, 58(7): 1436-1455. DOI: 10.7544/issn1000-1239.2021.20200685
    [6]Chen Jinyin, Chen Yipeng, Chen Yiming, Zheng Haibin, Ji Shouling, Shi Jie, Cheng Yao. Fairness Research on Deep Learning[J]. Journal of Computer Research and Development, 2021, 58(2): 264-280. DOI: 10.7544/issn1000-1239.2021.20200758
    [7]Zhu Hongrui, Yuan Guojun, Yao Chengji, Tan Guangming, Wang Zhan, Hu Zhongzhe, Zhang Xiaoyang, An Xuejun. Survey on Network of Distributed Deep Learning Training[J]. Journal of Computer Research and Development, 2021, 58(1): 98-115. DOI: 10.7544/issn1000-1239.2021.20190881
    [8]Zhang Rui, Li Jintao. A Survey on Algorithm Research of Scene Parsing Based on Deep Learning[J]. Journal of Computer Research and Development, 2020, 57(4): 859-875. DOI: 10.7544/issn1000-1239.2020.20190513
    [9]Xia Qing, Li Shuai, Hao Aimin, Zhao Qinping. Deep Learning for Digital Geometry Processing and Analysis: A Review[J]. Journal of Computer Research and Development, 2019, 56(1): 155-182. DOI: 10.7544/issn1000-1239.2019.20180709
    [10]Zhang Ruimao, Peng Jiefeng, Wu Yang, Lin Liang. The Semantic Knowledge Embedded Deep Representation Learning and Its Applications on Visual Understanding[J]. Journal of Computer Research and Development, 2017, 54(6): 1251-1266. DOI: 10.7544/issn1000-1239.2017.20171064
  • Cited by

    Periodical cited type(0)

    Other cited types(1)

Catalog

    Article views (1066) PDF downloads (517) Cited by(1)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return