article

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

Authors:

M. BronsteinAuthors Info & Claims

Computer Graphics Forum, Volume 35, Issue 6

Pages 87 - 119

https://doi.org/10.1111/cgf.12734

Published: 01 September 2016 Publication History

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.

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Viganò GMelzi S(2024)Bijective upsampling and learned embedding for point clouds correspondencesComputers and Graphics10.1016/j.cag.2024.103985122:COnline publication date: 1-Aug-2024
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Shui WWei PZheng XGeng S(2023)A Landmark-free Approach for Surface Asymmetry Detection and Profile Drawings from Bilaterally Symmetrical GeometryJournal on Computing and Cultural Heritage 10.1145/358924716:2(1-18)Online publication date: 28-Mar-2023
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cover image Computer Graphics Forum

Computer Graphics Forum Volume 35, Issue 6

September 2016

131 pages

ISSN:0167-7055

EISSN:1467-8659

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The Eurographs Association & John Wiley & Sons, Ltd.

Chichester, United Kingdom

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Published: 01 September 2016

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Viganò GMelzi S(2024)Bijective upsampling and learned embedding for point clouds correspondencesComputers and Graphics10.1016/j.cag.2024.103985122:COnline publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1016/j.cag.2024.103985
Yu ZChang YZhai SDeily NJu TWang XJammalamadaka UZhang NCalandrino JTroncoso C(2023)XCheckProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620395(2815-2832)Online publication date: 9-Aug-2023
https://dl.acm.org/doi/10.5555/3620237.3620395
Shui WWei PZheng XGeng S(2023)A Landmark-free Approach for Surface Asymmetry Detection and Profile Drawings from Bilaterally Symmetrical GeometryJournal on Computing and Cultural Heritage 10.1145/358924716:2(1-18)Online publication date: 28-Mar-2023
https://dl.acm.org/doi/10.1145/3589247
De Farias CTamadazte BStolkin RMarturi N(2022)Grasp Transfer for Deformable Objects by Functional Map Correspondence2022 International Conference on Robotics and Automation (ICRA)10.1109/ICRA46639.2022.9812141(735-741)Online publication date: 23-May-2022
https://dl.acm.org/doi/10.1109/ICRA46639.2022.9812141
Ma ZZhou JMa JLi T(2022)A novel 3D shape recognition method based on double-channel attention residual networkMultimedia Tools and Applications10.1007/s11042-022-12041-981:22(32519-32548)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11042-022-12041-9
Wang ZRosen D(2022)Manufacturing process classification based on heat kernel signature and convolutional neural networksJournal of Intelligent Manufacturing10.1007/s10845-022-02009-934:8(3389-3411)Online publication date: 30-Aug-2022
https://dl.acm.org/doi/10.1007/s10845-022-02009-9
Wu YYang J(2022)Multi-part shape matching by simultaneous partial functional correspondenceThe Visual Computer: International Journal of Computer Graphics10.1007/s00371-021-02337-639:1(393-412)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1007/s00371-021-02337-6
Sundararaman RPai GOvsjanikov M(2022)Implicit Field Supervision for Robust Non-rigid Shape MatchingComputer Vision – ECCV 202210.1007/978-3-031-20062-5_20(344-362)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20062-5_20
Vu TNguyen DHua BPham QYeung S(2022)RFNet-4D: Joint Object Reconstruction and Flow Estimation from 4D Point CloudsComputer Vision – ECCV 202210.1007/978-3-031-20050-2_3(36-52)Online publication date: 23-Oct-2022
https://dl.acm.org/doi/10.1007/978-3-031-20050-2_3
Zhang DWu ZWang XLv CZhou M(2021)3D non-rigid shape similarity measure based on Fréchet distance between spectral distance distribution curveMultimedia Tools and Applications10.1007/s11042-020-09420-580:1(615-640)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1007/s11042-020-09420-5
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