research-article

Deformation-driven topology-varying 3D shape correspondence

Authors:

Ibraheem Alhashim,

Patricio Simari,

Hao ZhangAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 34, Issue 6

Article No.: 236, Pages 1 - 13

https://doi.org/10.1145/2816795.2818088

Published: 02 November 2015 Publication History

Abstract

We present a deformation-driven approach to topology-varying 3D shape correspondence. In this paradigm, the best correspondence between two shapes is the one that results in a minimal-energy, possibly topology-varying, deformation that transforms one shape to conform to the other while respecting the correspondence. Our deformation model, called GeoTopo transform, allows both geometric and topological operations such as part split, duplication, and merging, leading to fine-grained and piecewise continuous correspondence results. The key ingredient of our correspondence scheme is a deformation energy that penalizes geometric distortion, encourages structure preservation, and simultaneously allows topology changes. This is accomplished by connecting shape parts using structural rods, which behave similarly to virtual springs but simultaneously allow the encoding of energies arising from geometric, structural, and topological shape variations. Driven by the combined deformation energy, an optimal shape correspondence is obtained via a pruned beam search. We demonstrate our deformation-driven correspondence scheme on extensive sets of man-made models with rich geometric and topological variation and compare the results to state-of-the-art approaches.

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Liu FLiu X(2024)Learning Implicit Functions for Dense 3D Shape Correspondence of Generic ObjectsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.323343146:3(1852-1867)Online publication date: Mar-2024
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Chaudhuri A(2024)Shape Deformation ModelsEncyclopedia of Computer Graphics and Games10.1007/978-3-031-23161-2_358(1637-1646)Online publication date: 5-Jan-2024
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Index Terms

Deformation-driven topology-varying 3D shape correspondence
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
2. Theory of computation
  1. Randomness, geometry and discrete structures

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 34, Issue 6

November 2015

944 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2816795

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Copyright © 2015 ACM.

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Publication History

Published: 02 November 2015

Published in TOG Volume 34, Issue 6

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Liu FLiu X(2024)Learning Implicit Functions for Dense 3D Shape Correspondence of Generic ObjectsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.323343146:3(1852-1867)Online publication date: Mar-2024
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Chaudhuri A(2024)Shape Deformation ModelsEncyclopedia of Computer Graphics and Games10.1007/978-3-031-23161-2_358(1637-1646)Online publication date: 5-Jan-2024
https://doi.org/10.1007/978-3-031-23161-2_358
Xu PLi YYang ZShi WFu HHuang H(2022)Hierarchical Layout Blending with Recursive Optimal CorrespondenceACM Transactions on Graphics10.1145/3550454.355544641:6(1-15)Online publication date: 30-Nov-2022
https://dl.acm.org/doi/10.1145/3550454.3555446
Zhang HWang LWang YWang NNing XLv K(2021)Multi-Material Interface Extraction and its Topological Relationship Expression for 3-D Image DataIEEE Transactions on Instrumentation and Measurement10.1109/TIM.2021.311198770(1-9)Online publication date: 2021
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Su FZhu HChen TLi LYang FPeng HTang LZuo XLiang YYing S(2021)An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point cloudsISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2020.12.007172(114-131)Online publication date: Feb-2021
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Liu FLiu XLarochelle HRanzato MHadsell RBalcan MLin H(2020)Learning implicit functions for topology-varying dense 3D shape correspondenceProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3496129(4823-4834)Online publication date: 6-Dec-2020
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Lang XSun Z(2020)Structure-aware shape correspondence network for 3D shape synthesisComputer Aided Geometric Design10.1016/j.cagd.2020.10185779(101857)Online publication date: May-2020
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