Efficient deformable shape correspondence via kernel matching

Matthias Vestner; Zorah Lahner; Amit Boyarski; Or Litany; Ron Slossberg; Tal Remez; Emanuele Rodola; Alex Bronstein; Michael Bronstein; Ron Kimmel; Daniel Cremers

doi:10.1109/3DV.2017.00065

Efficient deformable shape correspondence via kernel matching

Matthias Vestner, Zorah Lahner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodola, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers

Informatics 9 - Chair of Computer Vision and Artificial Intelligence

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

81 Scopus citations

Abstract

We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-wise and point-wise descriptors, imposing a continuity prior on the mapping, and propose a projected descent optimization procedure inspired by difference of convex functions (DC) programming.

Original language	English
Title of host publication	Proceedings - 2017 International Conference on 3D Vision, 3DV 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	517-526
Number of pages	10
ISBN (Electronic)	9781538626108
DOIs	https://doi.org/10.1109/3DV.2017.00065
State	Published - 25 May 2018
Event	7th IEEE International Conference on 3D Vision, 3DV 2017 - Qingdao, China Duration: 10 Oct 2017 → 12 Oct 2017

Publication series

Name	Proceedings - 2017 International Conference on 3D Vision, 3DV 2017

Conference

Conference	7th IEEE International Conference on 3D Vision, 3DV 2017
Country/Territory	China
City	Qingdao
Period	10/10/17 → 12/10/17

Keywords

Non-Rigid-Shapes
Shape-Correspondence

Access to Document

10.1109/3DV.2017.00065

Cite this

Vestner, M., Lahner, Z., Boyarski, A., Litany, O., Slossberg, R., Remez, T., Rodola, E., Bronstein, A., Bronstein, M., Kimmel, R., & Cremers, D. (2018). Efficient deformable shape correspondence via kernel matching. In Proceedings - 2017 International Conference on 3D Vision, 3DV 2017 (pp. 517-526). (Proceedings - 2017 International Conference on 3D Vision, 3DV 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/3DV.2017.00065

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title = "Efficient deformable shape correspondence via kernel matching",

abstract = "We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-wise and point-wise descriptors, imposing a continuity prior on the mapping, and propose a projected descent optimization procedure inspired by difference of convex functions (DC) programming.",

keywords = "Non-Rigid-Shapes, Shape-Correspondence",

author = "Matthias Vestner and Zorah Lahner and Amit Boyarski and Or Litany and Ron Slossberg and Tal Remez and Emanuele Rodola and Alex Bronstein and Michael Bronstein and Ron Kimmel and Daniel Cremers",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 7th IEEE International Conference on 3D Vision, 3DV 2017 ; Conference date: 10-10-2017 Through 12-10-2017",

year = "2018",

month = may,

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doi = "10.1109/3DV.2017.00065",

language = "English",

series = "Proceedings - 2017 International Conference on 3D Vision, 3DV 2017",

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Vestner, M, Lahner, Z, Boyarski, A, Litany, O, Slossberg, R, Remez, T, Rodola, E, Bronstein, A, Bronstein, M, Kimmel, R & Cremers, D 2018, Efficient deformable shape correspondence via kernel matching. in Proceedings - 2017 International Conference on 3D Vision, 3DV 2017. Proceedings - 2017 International Conference on 3D Vision, 3DV 2017, Institute of Electrical and Electronics Engineers Inc., pp. 517-526, 7th IEEE International Conference on 3D Vision, 3DV 2017, Qingdao, China, 10/10/17. https://doi.org/10.1109/3DV.2017.00065

Efficient deformable shape correspondence via kernel matching. / Vestner, Matthias; Lahner, Zorah; Boyarski, Amit et al.
Proceedings - 2017 International Conference on 3D Vision, 3DV 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 517-526 (Proceedings - 2017 International Conference on 3D Vision, 3DV 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Efficient deformable shape correspondence via kernel matching

AU - Vestner, Matthias

AU - Lahner, Zorah

AU - Boyarski, Amit

AU - Litany, Or

AU - Slossberg, Ron

AU - Remez, Tal

AU - Rodola, Emanuele

AU - Bronstein, Alex

AU - Bronstein, Michael

AU - Kimmel, Ron

AU - Cremers, Daniel

PY - 2018/5/25

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N2 - We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-wise and point-wise descriptors, imposing a continuity prior on the mapping, and propose a projected descent optimization procedure inspired by difference of convex functions (DC) programming.

AB - We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality. We formulate the problem as matching between a set of pair-wise and point-wise descriptors, imposing a continuity prior on the mapping, and propose a projected descent optimization procedure inspired by difference of convex functions (DC) programming.

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SP - 517

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BT - Proceedings - 2017 International Conference on 3D Vision, 3DV 2017

PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Vestner M, Lahner Z, Boyarski A, Litany O, Slossberg R, Remez T et al. Efficient deformable shape correspondence via kernel matching. In Proceedings - 2017 International Conference on 3D Vision, 3DV 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 517-526. (Proceedings - 2017 International Conference on 3D Vision, 3DV 2017). doi: 10.1109/3DV.2017.00065

Efficient deformable shape correspondence via kernel matching

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Keywords

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