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A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts.

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Τίτλος: A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts.
Συγγραφείς: Lu, Jiaxin, Liang, Yongqing, Han, Huijun, Hua, Jiacheng, Jiang, Junfeng, Li, Xin, Huang, Qixing
Πηγή: Computer Graphics Forum; May2025, Vol. 44 Issue 2, p1-32, 32p
Θεματικοί όροι: Computer graphics, Algorithms, Deep learning, Image segmentation, Shape recognition (Computer vision)
Περίληψη: Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. This reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general‐purpose approaches. We also survey the trends from early procedural approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open‐source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics. [ABSTRACT FROM AUTHOR]
Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts.
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  Data: <searchLink fieldCode="AR" term="%22Lu%2C+Jiaxin%22">Lu, Jiaxin</searchLink><br /><searchLink fieldCode="AR" term="%22Liang%2C+Yongqing%22">Liang, Yongqing</searchLink><br /><searchLink fieldCode="AR" term="%22Han%2C+Huijun%22">Han, Huijun</searchLink><br /><searchLink fieldCode="AR" term="%22Hua%2C+Jiacheng%22">Hua, Jiacheng</searchLink><br /><searchLink fieldCode="AR" term="%22Jiang%2C+Junfeng%22">Jiang, Junfeng</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Xin%22">Li, Xin</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Qixing%22">Huang, Qixing</searchLink>
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  Data: Computer Graphics Forum; May2025, Vol. 44 Issue 2, p1-32, 32p
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  Data: <searchLink fieldCode="DE" term="%22Computer+graphics%22">Computer graphics</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Shape+recognition+%28Computer+vision%29%22">Shape recognition (Computer vision)</searchLink>
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  Label: Abstract
  Group: Ab
  Data: Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. This reassembly problem requires understanding the attributes of individual pieces and establishing matches between different pieces. Many approaches also model priors of the underlying complete object. Existing approaches are tightly connected problems of shape segmentation, shape matching, and learning shape priors. We provide existing algorithms in this context and emphasize their similarities and differences to general‐purpose approaches. We also survey the trends from early procedural approaches to more recent deep learning approaches. In addition to algorithms, this survey will also describe existing datasets, open‐source software packages, and applications. To the best of our knowledge, this is the first comprehensive survey on this topic in computer graphics. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Computer Graphics Forum is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2025
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              Y: 2025
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