Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment

The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similar...

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Vydáno v:Computer graphics forum Ročník 35; číslo 6; s. 87 - 119
Hlavní autoři: Biasotti, S., Cerri, A., Bronstein, A., Bronstein, M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.09.2016
Témata:
3-D graphics
3D shape distances
3D shape matching
Analysis
Assessments
Classification
Deformation
I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling
I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling; I.3.6 [Computer Graphics]: Methodology and Techniques
I.3.6 [Computer Graphics]: Methodology and Techniques
Image processing systems
map-based correspondence
Mathematical analysis
Mathematical models
Segmentation
Shape recognition
Similarity
Studies
ISSN:0167-7055, 1467-8659
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Abstract The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map‐based methods also define (dense) shape correspondences. After presenting in detail the theoretical foundations underlying these approaches, we classify them by looking at their most salient features, including the kind of structure and invariance properties they capture, as well as the distances and the output modalities according to which the similarity between shapes is assessed and returned. We also review the usage of these methods in a number of 3D shape application domains, ranging from matching and retrieval to annotation and segmentation. Finally, the most promising directions for future research developments are discussed. The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map‐based methods also define (dense) shape correspondences.
AbstractList The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map‐based methods also define (dense) shape correspondences. After presenting in detail the theoretical foundations underlying these approaches, we classify them by looking at their most salient features, including the kind of structure and invariance properties they capture, as well as the distances and the output modalities according to which the similarity between shapes is assessed and returned. We also review the usage of these methods in a number of 3D shape application domains, ranging from matching and retrieval to annotation and segmentation. Finally, the most promising directions for future research developments are discussed.
The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map‐based methods also define (dense) shape correspondences. After presenting in detail the theoretical foundations underlying these approaches, we classify them by looking at their most salient features, including the kind of structure and invariance properties they capture, as well as the distances and the output modalities according to which the similarity between shapes is assessed and returned. We also review the usage of these methods in a number of 3D shape application domains, ranging from matching and retrieval to annotation and segmentation. Finally, the most promising directions for future research developments are discussed. The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map‐based methods also define (dense) shape correspondences.
The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map-based methods also define (dense) shape correspondences. After presenting in detail the theoretical foundations underlying these approaches, we classify them by looking at their most salient features, including the kind of structure and invariance properties they capture, as well as the distances and the output modalities according to which the similarity between shapes is assessed and returned. We also review the usage of these methods in a number of 3D shape application domains, ranging from matching and retrieval to annotation and segmentation. Finally, the most promising directions for future research developments are discussed. The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions yields a new interpretation of the 3D shape similarity assessment and opens new perspectives. Indeed, while the classical approaches to similarity mainly quantify it as a numerical score, map-based methods also define (dense) shape correspondences.
Author Bronstein, M.
Cerri, A.
Biasotti, S.
Bronstein, A.
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  email: bron@eng.tau.ac.il
  organization: School of Electrical Engineering, Tel Aviv University, Israel
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  givenname: M.
  surname: Bronstein
  fullname: Bronstein, M.
  email: michael.bronstein@usi.ch
  organization: Institute of Computational Science, University of Lugano (USI), Switzerland
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PublicationCentury 2000
PublicationDate September 2016
PublicationDateYYYYMMDD 2016-09-01
PublicationDate_xml – month: 09
  year: 2016
  text: September 2016
PublicationDecade 2010
PublicationPlace Oxford
PublicationPlace_xml – name: Oxford
PublicationTitle Computer graphics forum
PublicationTitleAlternate Computer Graphics Forum
PublicationYear 2016
Publisher Blackwell Publishing Ltd
Publisher_xml – name: Blackwell Publishing Ltd
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Snippet The recent introduction of 3D shape analysis frameworks able to quantify the deformation of a shape into another in terms of the variation of real functions...
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SubjectTerms 3-D graphics
3D shape distances
3D shape matching
Analysis
Assessments
Classification
Deformation
I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling
I.3.5 [Computer Graphics]: Computational Geometry and Object Modelling; I.3.6 [Computer Graphics]: Methodology and Techniques
I.3.6 [Computer Graphics]: Methodology and Techniques
Image processing systems
map-based correspondence
Mathematical analysis
Mathematical models
Segmentation
Shape recognition
Similarity
Studies
Title Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment
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Volume 35
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