Hierarchical mesh decomposition using fuzzy clustering and cuts

By Sagi Katz, Ayellet Tal. ACM Transactions on Graphics, 2003

752 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

View Paper: https://www.semanticscholar.org/paper/cfbf95199cdf1c4e58c6ce453e2e6d47dcae6f70

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

Consistent mesh partitioning and skeletonisation using the shape diameter function (2008, background): “These attributes include curvature and geodesic distances [13, 16, 21, 25, 26, 37], dihedral angles [28], planarity and normal direction [3, 5], slippage [10] etc.”
3D Skeletons: A State‐of‐the‐Art Report (2016, background): “19a), while in animated articulated models concavities close to bending joints create hints to hierarchically segment the shape [KT03].”
Contextual Part Analogies in 3D Objects (2010, methodology): “Nevertheless, other part-type partitioning methods such as Attene et al. (2006b), Katz and Tal (2003) could also be used in the first stage of our algorithm.”
Model Composition from Interchangeable Components (2007, methodology): “Geodesic distances are commonly used to steer the segmentation and negative curvature is taken into account when generating the actual cuts [17, 25, 16].”
Unsupervised co-segmentation for 3D shapes using iterative multi-label optimization (2013, neutral): “To handle the non-homogenous part stretching of shapes, Xu et al. [7] firstly group the shapes based on their styles and then perform part correspondences in each style group.”
3D Mesh Labeling via Deep Convolutional Neural Networks (2015, background): “In some early studies such as Katz and Tal [2003], Ben-Chen Gotsman [2008], Huang et al. [2009], Shapira et al. [2010], Zhang et al. [2012], and Au et al. [2012], a major concern is to ﬁnd a single feature that is effective for mesh label identiﬁcation.”
3D Shape Segmentation and Labeling via Extreme Learning Machine (2014, positive): “Given a mesh, we ﬁrst employ the normalized cuts [GF08] to decompose it into primitive patches, and then optimize the patch boundaries to align them against shape features using fuzzy cuts [KT03].”
Curve-Skeleton Properties, Applications, and Algorithms (2007, methodology): “Katz and Tal [59] first decompose a mesh surface into segments using clustering and then use this segmentation to construct a skeleton (joints are represented by centered vertices at the boundary between different patches).”
Fast exact and approximate geodesics on meshes (2005, methodology): “Geodesic paths also are needed when segmenting a mesh into subparts, such as done in [ KT03 ].”
Style-content separation by anisotropic part scales (2010, methodology): “[2008] rely on the shape diameter function and Kraevoy et al. [2007] on convexity and compactness to consistently segment organic shapes including humans and animals.”
Curve skeleton extraction from incomplete point cloud (2009, methodology): “Many algorithms for curve skeleton extraction exist [Cornea et al. 2007], dealing mostly with shapes speciﬁed by closed polygonal meshes [Hilaga et al. 2001; Katz and Tal 2003; Chuang et al. 2004; Dey and Sun 2006; Au et al. 2008].”
Modeling by example (2004, methodology): “Figure 11 compares our intelligent scissoring results for a cheetah and a hand with results reported by [Katz and Tal 2003].”
PointGrid: A Deep Network for 3D Shape Understanding (2018, methodology): “k-NN, random forest, SVM [14], JointBoost classifier [23], correspondence analysis [56, 62]) or employ some local and greedy methods such as region growing [52, 20], hierarchical clustering [3, 11, 24], spectral clustering [46], k-means [60], normalized cut [13], random walk [29] and heat walk [5].”