research-article

Active co-analysis of a set of shapes

Authors:

Oliver van Kaick,

Daniel Cohen-Or,

Baoquan ChenAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 31, Issue 6

Article No.: 165, Pages 1 - 10

https://doi.org/10.1145/2366145.2366184

Published: 01 November 2012 Publication History

Abstract

Unsupervised co-analysis of a set of shapes is a difficult problem since the geometry of the shapes alone cannot always fully describe the semantics of the shape parts. In this paper, we propose a semi-supervised learning method where the user actively assists in the co-analysis by iteratively providing inputs that progressively constrain the system. We introduce a novel constrained clustering method based on a spring system which embeds elements to better respect their inter-distances in feature space together with the user-given set of constraints. We also present an active learning method that suggests to the user where his input is likely to be the most effective in refining the results. We show that each single pair of constraints affects many relations across the set. Thus, the method requires only a sparse set of constraints to quickly converge toward a consistent and error-free semantic labeling of the set.

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Cited By

Pearl OLang IHu YYeh RHanocka R(2025)GeoCode: Interpretable Shape ProgramsComputer Graphics Forum10.1111/cgf.15276Online publication date: 12-Feb-2025
https://doi.org/10.1111/cgf.15276
Shu ZLi SXin SLiu L(2025)3D Shape Segmentation With Potential Consistency Mining and EnhancementIEEE Transactions on Multimedia10.1109/TMM.2024.352167427(133-144)Online publication date: 2025
https://doi.org/10.1109/TMM.2024.3521674
Xie MZhao JShen K(2025)A Novel SO(3) Rotational Equivariant Masked Autoencoder for 3D Mesh Object AnalysisIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.346504135:1(329-342)Online publication date: Jan-2025
https://doi.org/10.1109/TCSVT.2024.3465041
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Index Terms

Active co-analysis of a set of shapes
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
2. Theory of computation
  1. Randomness, geometry and discrete structures
    1. Computational geometry

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Published In

cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 31, Issue 6

November 2012

794 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2366145

Issue’s Table of Contents

Copyright © 2012 ACM.

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Publication History

Published: 01 November 2012

Published in TOG Volume 31, Issue 6

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Funding Sources

Natural Sciences and Engineering Research Council of Canada
Shenzhen Science and Innovation Program
Guangdong Science and Technology Program
National Natural Science Foundation of China
Ministry of Science and Technology of the People's Republic of China

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Cited By

Pearl OLang IHu YYeh RHanocka R(2025)GeoCode: Interpretable Shape ProgramsComputer Graphics Forum10.1111/cgf.15276Online publication date: 12-Feb-2025
https://doi.org/10.1111/cgf.15276
Shu ZLi SXin SLiu L(2025)3D Shape Segmentation With Potential Consistency Mining and EnhancementIEEE Transactions on Multimedia10.1109/TMM.2024.352167427(133-144)Online publication date: 2025
https://doi.org/10.1109/TMM.2024.3521674
Xie MZhao JShen K(2025)A Novel SO(3) Rotational Equivariant Masked Autoencoder for 3D Mesh Object AnalysisIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2024.346504135:1(329-342)Online publication date: Jan-2025
https://doi.org/10.1109/TCSVT.2024.3465041
He YYu HLiu XYang ZSun WAnwar SMian A(2025)Deep learning based 3D segmentation in computer vision: A surveyInformation Fusion10.1016/j.inffus.2024.102722115(102722)Online publication date: Mar-2025
https://doi.org/10.1016/j.inffus.2024.102722
Chen SXu RXu JXin STu CYang CLu L(2024)QuickCSGModeling: Quick CSG Operations Based on Fusing Signed Distance Fields for VR ModelingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/359972920:7(1-18)Online publication date: 27-Mar-2024
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Yu DXiao CLau MFu H(2024)Sketch2Stress: Sketching With Structural Stress AwarenessIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.334211930:10(6851-6865)Online publication date: 1-Oct-2024
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Zhuang JZeng PZhuang WGuo XLiu P(2024)Supervertex Sampling Network: A Geodesic Differential SLIC Approach for 3D MeshIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.329484530:8(5553-5565)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3294845
Lei NLi ZXu ZLi YGu X(2024)What's the Situation With Intelligent Mesh Generation: A Survey and PerspectivesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.328178130:8(4997-5017)Online publication date: 1-Aug-2024
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Dong QWang ZLi MGao JChen SShu ZXin STu CWang W(2024)Laplacian2Mesh: Laplacian-Based Mesh UnderstandingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.325904430:7(4349-4361)Online publication date: Jul-2024
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Li XLiu ZChen TMu TMartin RHu S(2024)Mesh Neural Networks Based on Dual Graph PyramidsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.325703530:7(4211-4224)Online publication date: 1-Jul-2024
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