SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topology matching for fully automatic similarity estimation of 3D shapes
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Nonmonotone Spectral Projected Gradient Methods on Convex Sets
SIAM Journal on Optimization
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical mesh decomposition using fuzzy clustering and cuts
ACM SIGGRAPH 2003 Papers
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Salient geometric features for partial shape matching and similarity
ACM Transactions on Graphics (TOG)
Mesh Segmentation - A Comparative Study
SMI '06 Proceedings of the IEEE International Conference on Shape Modeling and Applications 2006
Model Composition from Interchangeable Components
PG '07 Proceedings of the 15th Pacific Conference on Computer Graphics and Applications
Reeb graphs for shape analysis and applications
Theoretical Computer Science
Randomized cuts for 3D mesh analysis
ACM SIGGRAPH Asia 2008 papers
A benchmark for 3D mesh segmentation
ACM SIGGRAPH 2009 papers
Technical Section: Consistent segmentation of 3D models
Computers and Graphics
Mesh scissoring with minima rule and part salience
Computer Aided Geometric Design - Special issue: Geometry processing
Learning 3D mesh segmentation and labeling
ACM SIGGRAPH 2010 papers
Contextual Part Analogies in 3D Objects
International Journal of Computer Vision
Style-content separation by anisotropic part scales
ACM SIGGRAPH Asia 2010 papers
Joint shape segmentation with linear programming
Proceedings of the 2011 SIGGRAPH Asia Conference
Unsupervised co-segmentation of a set of shapes via descriptor-space spectral clustering
Proceedings of the 2011 SIGGRAPH Asia Conference
Multi-task low-rank affinity pursuit for image segmentation
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Characterizing shape using conformal factors
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
Active co-analysis of a set of shapes
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization
Computer-Aided Design
Learning part-based templates from large collections of 3D shapes
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Co-segmentation of 3D shapes via multi-view spectral clustering
The Visual Computer: International Journal of Computer Graphics
SMI 2013: New evaluation metrics for mesh segmentation
Computers and Graphics
SMI 2013: Grouping real functions defined on 3D surfaces
Computers and Graphics
Structure-aware shape processing
SIGGRAPH Asia 2013 Courses
Special Section on CAD/Graphics 2013: Confidence-driven image co-matting
Computers and Graphics
CAD/Graphics 2013: Interactive shape co-segmentation via label propagation
Computers and Graphics
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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.