Metamorphosis of Polyhedral Surfaces using Decomposition

By Sh.M. Shlafman, Ayellet Tal, Sagi Katz. Computer graphics forum (Print), 2002

297 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/8d642e138e5beae59911220f545a3b7df0762a4f

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

A benchmark for 3D mesh segmentation (2009, methodology): “The following paragraphs provide a brief description of each algorithm, but please refer to the cited papers for details: • K-Means: Shlafman et al. [Shlafman et al. 2002] describe an algorithm based on K-means clustering of faces.”
Hierarchical mesh decomposition using fuzzy clustering and cuts (2003, methodology): “In [Shlafman et al. 2002] a -means based clustering algorithm is proposed.”
Multi-Chart Geometry Images (2003, background): “Shlafman et al. [2002] also develop a clustering-based approach to mesh partitioning.”
Learning 3D mesh segmentation and labeling (2010, methodology): “Most effort has focused on finding simple geometric criteria for segmentation of a single input mesh [Mangan and Whitaker 1999; Shlafman et al. 2002; Katz and Tal 2003; Liu and Zhang 2004; Katz et al. 2005; Simari et al. 2006; Attene et al. 2006b; Lin et al. 2007; Golovinskiy and Funkhouser 2008;…”
Mesh Segmentation - A Comparative Study (2006, background): “Semantics-oriented approaches to shape segmentation have gained a great interest recently in the research community [5, 20, 28, 19, 34, 32, 15, 18, 16, 14, 25], because they can support parametrization or re-meshing schemes, metamorphosis, 3D shape retrieval, skeleton extraction as well as the model”
Min-cut based segmentation of point clouds (2009, methodology): “A wide variety of algorithms have been proposed for this problem, including ones based on convex decomposition [2], watershed analysis [12], K-means [17], hierarchical clustering [4, 5], region growing [19], and spectral clustering [11].”
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.”
Mesh segmentation using feature point and core extraction (2005, methodology): “Many methods use clustering techniques, such as region growing [4, 24], iterative clustering [31], spectral clustering [23], feature point-based clustering [33, 35], and fuzzy clustering with graph cuts [15].”