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Co-Segmentation of 3D Shapes via Subspace Clustering

Authors:

Ligang LiuAuthors Info & Claims

Computer Graphics Forum, Volume 31, Issue 5

Pages 1703 - 1713

https://doi.org/10.1111/j.1467-8659.2012.03175.x

Published: 01 August 2012 Publication History

Abstract

We present a novel algorithm for automatically co-segmenting a set of shapes from a common family into consistent parts. Starting from over-segmentations of shapes, our approach generates the segmentations by grouping the primitive patches of the shapes directly and obtains their correspondences simultaneously. The core of the algorithm is to compute an affinity matrix where each entry encodes the similarity between two patches, which is measured based on the geometric features of patches. Instead of concatenating the different features into one feature descriptor, we formulate co-segmentation into a subspace clustering problem in multiple feature spaces. Specifically, to fuse multiple features, we propose a new formulation of optimization with a consistent penalty, which facilitates both the identification of most similar patches and selection of master features for two similar patches. Therefore the affinity matrices for various features are sparsity-consistent and the similarity between a pair of patches may be determined by part of (instead of all) features. Experimental results have shown how our algorithm jointly extracts consistent parts across the collection in a good manner. © 2012 Wiley Periodicals, Inc.

References

[1]

Attene M., Katz S., Mortara M., Patane G., Spagnuolo M., Tal A.: Mesh segmentation – a comparative study. In Proc. IEEE International Conference on Shape Modeling and Applications (2006), pp. 1–7. 1, 2.

Digital Library

[2]

Ben-Chen M., Gotsman C.: Characterizing shape using conformal factors. In Proc. Eurographics Workshop on Shape Retrieval (2008), pp. 1–8. 3.

Digital Library

[3]

Biasotti S., Giorgi D., Spagnuolo M., Falcidieno B.: Reeb graphs for shape analysis and applications. Theoretical Computer Science 392, 1–3 (2008), 5–22. 10.

Digital Library

[4]

Belongie S., Malik J., Puzicha J.: Shape matching and object recognition using shape contexts. IEEE Trans. on Pattern Analysis and Machine Intelligence 24, 4 (2002), 509–522. 3.

Digital Library

[5]

Birgin E., Martínez J., Raydan M.: Nonmonotone spectral projected gradient methods on convex sets. SIAM Journal on Optimization 10, 4 (2000), 1196–1211. 6.

Digital Library

[6]

Chen X., Golovinskiy A., Funkhouser T.: A benchmark for 3D mesh segmentation. ACM Transactions on Graphics (Proc. SIGGRAPH) 28, 3 (2009), 73:1–12. 1, 2, 7.

Digital Library

[7]

Cheng B., Liu G., Wang J., Huang Z., Yan S.: Multi-task low-rank affinity pursuit for image segmentation. In Proc. IEEE ICCV (2011), pp. 1–8. 2, 6.

Digital Library

[8]

Cour T., Yu S., Shi J.: Matlab normalized cuts segmentation code. URL: http://www.cis.upenn.edu/~jshi/software/. 6.

[9]

Donoho D.: Compressed sensing. IEEE Transactions on Information Theory 52, 4 (2006), 1289–1306. 4.

Digital Library

[10]

Elhamifar E., Vidal R.: Sparse subspace clustering. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (2009), pp. 2790–2797. 4.

[11]

Friedman J., Hastie T., Tibshirani R.: A note on the group lasso and a sparse group lasso. Arxiv preprint arXiv:1001.0736 (2010). 6.

[12]

Gal R., Cohen-Or D.: Salient geometric features for partial shape matching and similarity. ACM Trans. Graph. (2006), 130–150. 3.

Digital Library

[13]

Golovinskiy A., Funkhouser T.: Randomized cuts for 3D mesh analysis. ACM Transactions on Graphics (Proc. SIG-GRAPH ASIA) 27, 5 (2008), 145:1–10. 3.

Digital Library

[14]

Golovinskiy A., Funkhouser T.: Consistent segmentation of 3D models. Computers and Graphics (Proc. Shape Modeling International) 33, 3 (2009), 262–269. 2, 3.

Digital Library

[15]

Huang Q., Koltun V., Guibas L.: Joint-shape segmentation with linear programming. ACM Transactions on Graphics (Proc. SIGGRAPH ASIA) 30, 6 (2011), 125:1–11. 2, 3.

Digital Library

[16]

Hilaga M., Shinagawa Y., Kohmura T., Kunii T. L.: Topology matching for fully automatic similarity estimation of 3d shapes. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques (New York, NY, USA, 2001), SIGGRAPH '01, ACM, pp. 203–212. 2, 3.

Digital Library

[17]

Kalogerakis E., Hertzmann A., Singh K.: Learning 3D mesh segmentation and labeling. ACM Trans. on Graphics (Proc. SIGGRAPH) 29, 3 (2010), 102:1–10. 2, 3, 8, 9, 10.

Digital Library

[18]

Kraevoy V., Julius D., Sheffer A.: Model composition from interchangeable components. In Proc. Pacific Graphics (2007), pp. 129–138. 3.

Digital Library

[19]

Katz S., Tal A.: Hierarchical mesh decomposition using fuzzy clustering and cuts. ACM Transactions on Graphics (Proc. SIGGRAPH) 22, 3 (2003), 954–961. 6.

Digital Library

[20]

Lee Y., Lee S., Shamir A., Cohen-Or D., Seidel H.-P.: Mesh scissoring with minima rule and part salience. Computer Aided Geometric Design (2005), 444–465. 6.

Digital Library

[21]

Meng M., Xia J., Luo J., He Y.: Unsupervised co-segmentation for 3dshapes using iterative multi-label optimization. Computer-Aided Design (Proc. ACM Symposium on Solid and Physical Modeling) (2012). 3.

[22]

Parsons L., Haque E., Liu H.: Subspace clustering for high dimensional data: a review. ACM SIGKDD Explorations Newsletter 6, 1 (2004), 90–105. 3.

Digital Library

[23]

Ren X., Malik J.: Learning a classification model for segmentation. In Proc. IEEE ICCV (2003), pp. 10–17. 3.

Digital Library

[24]

Shamir A.: A survey on mesh segmentation techniques. Computer Graphics Forum 27, 6 (2008), 1539–1556. 1, 2.

[25]

Shi J., Malik J.: Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 8 (2000), 888–905. 3, 4, 5, 6.

Digital Library

[26]

Shapira L., Shalom S., Shamir A., Cohen-Or D., Zhang H.: Contextual part analogies in 3D objects. International Journal of Computer Vision 89, 2–3 (2010), 309–326. 2, 3.

Digital Library

[27]

Sidi O., Van Kaick O., Kleiman Y., Zhang H., Cohen-Or D.: Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering. ACM Trans. on Graph. (Proc. SIGGRAPH ASIA) 30, 6 (2011), 126:1–9. 2, 3, 7, 8, 9, 10.

Digital Library

[28]

Turk G.: Re-tiling polygonal surfaces. In Proc. of SIGGRAPH (1992), pp. 55–64. 7.

Digital Library

[29]

Vidal R.: A tutorial on subspace clustering. IEEE Signal Processing Magazine 28, 2 (2010), 52–68. 2, 3, 4.

[30]

Van Kaick O., Tagliasacchi A., Sidi O., Zhang H., Cohen-Or D., Wolf L., Hamarneh G.: Prior knowledge for part correspondence. Computer Graphics Forum (Proc. Eurographics) 30, 2 (2011), 553–562. 3.

[31]

Van Kaick O., Zhang H., Hamarneh G., Cohen-Or D.: A survey on shape correspondence. In Proc. of Eurographics State-of-the-art Report (2010), pp. 1–24. 2.

[32]

Wang S., Yuan X., Yao T., Yan S., Shen J.: Efficient subspace segmentation via quadratic programming. In Proc. AAAI Artificial Intelligence (2011), pp. 519–524. 4.

[33]

Xu K., Li H., Zhang H., Cohen-Or D., Xiong Y., Cheng Z.: Style-content separation by anisotropic part scales. ACM Transactions on Graphics (Proc. SIGGRAPH ASIA) 29, 5 (2010), 184:1–10. 2, 3.

Digital Library

Cited By

Zhang RPan SLv CGong MHuang H(2024)Architectural Co-LOD GenerationACM Transactions on Graphics10.1145/368790543:6(1-16)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687905
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
Umam AYang CChuang JLin Y(2024)Unsupervised Point Cloud Co-Part Segmentation via Co-Attended Superpoint Generation and AggregationIEEE Transactions on Multimedia10.1109/TMM.2024.337129426(7775-7786)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3371294
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Co-Segmentation of 3D Shapes via Subspace Clustering
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning

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Information & Contributors

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Published In

cover image Computer Graphics Forum

Computer Graphics Forum Volume 31, Issue 5

August 2012

224 pages

ISSN:0167-7055

EISSN:1467-8659

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Publisher

The Eurographs Association & John Wiley & Sons, Ltd.

Chichester, United Kingdom

Publication History

Published: 01 August 2012

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I.4.6 [Computer Graphics]: Segmentation—

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Cited By

Zhang RPan SLv CGong MHuang H(2024)Architectural Co-LOD GenerationACM Transactions on Graphics10.1145/368790543:6(1-16)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687905
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
Umam AYang CChuang JLin Y(2024)Unsupervised Point Cloud Co-Part Segmentation via Co-Attended Superpoint Generation and AggregationIEEE Transactions on Multimedia10.1109/TMM.2024.337129426(7775-7786)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1109/TMM.2024.3371294
Shu ZWu TShen JXin SLiu L(2024)Semi-Supervised 3D Shape Segmentation via Self RefiningIEEE Transactions on Image Processing10.1109/TIP.2024.337420033(2044-2057)Online publication date: 12-Mar-2024
https://dl.acm.org/doi/10.1109/TIP.2024.3374200
Yaseen HMahmood A(2023)Learning Structure Aware Deep Spectral EmbeddingIEEE Transactions on Image Processing10.1109/TIP.2023.328207432(3939-3948)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TIP.2023.3282074
Krawczyk DSitnik R(2023)Segmentation of 3D Point Cloud Data Representing Full Human Body GeometryPattern Recognition10.1016/j.patcog.2023.109444139:COnline publication date: 10-May-2023
https://dl.acm.org/doi/10.1016/j.patcog.2023.109444
Lin CLiu LLi CKobbelt LWang BXin SWang W(2022)SEG-MAT: 3D Shape Segmentation Using Medial Axis TransformIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.303256628:6(2430-2444)Online publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1109/TVCG.2020.3032566
Liu YLong WShu ZYi SXin S(2022)Voxel-Based 3D Shape Segmentation Using Deep Volumetric Convolutional Neural NetworksAdvances in Computer Graphics10.1007/978-3-031-23473-6_38(489-500)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-23473-6_38
Sun CZou QTong XLiu Y(2019)Learning adaptive hierarchical cuboid abstractions of 3D shape collectionsACM Transactions on Graphics10.1145/3355089.335652938:6(1-13)Online publication date: 8-Nov-2019
https://dl.acm.org/doi/10.1145/3355089.3356529
Yang JWang BWang MKe Y(2019)Unsupervised Co-segmentation of 3D Shapes Based on ComponentsProceedings of the 2nd International Conference on Computer Science and Software Engineering10.1145/3339363.3339386(89-95)Online publication date: 24-May-2019
https://dl.acm.org/doi/10.1145/3339363.3339386
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