Mesh segmentation using feature point and core extraction

By Sagi Katz, George Leifman, Ayellet Tal. The Visual Computer, 2005

427 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1b6722944cc4c9ab11a837273ce2732cacb4f38e

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Technical Section: Discrete Laplace-Beltrami operators for shape analysis and segmentation (2009, methodology): “Spectral methods have recently gained much interest in computer graphics [62], with applications that include mesh compression [30], parametrization [25, 43], segmentation [31, 35], remeshing [17], filtering [34, 57], correspondence [27], matching and retrieval [28, 49, 52], manifold learning [5], a”
Consistent mesh partitioning and skeletonisation using the shape diameter function (2008, methodology): “using multi-dimensional scaling [6]) and then apply the partitioning [ 11 ].”
Laplace-Beltrami eigenfunctions for deformation invariant shape representation (2007, neutral): “Such representations were used for shape classiﬁcation [EK03,JZ07], part correspondence [JZ06], and segmentation [KLT05].”
Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes (2011, background): “Some tasks that benefit from this capability are object registration [5], object retrieval and matching [9], mesh simplification, viewpoint selection [14], and mesh segmentation [11, 25], just to name a few.”
A benchmark for 3D mesh segmentation (2009, methodology): “• Core extraction: Katz et al. [Katz et al. 2005] propose a hierarchical decomposition algorithm that performs four main steps at each stage: transformation of the mesh vertices into a pose insensitive representation using multidimensional scaling, extraction of prominent feature points, extraction…”
Learning 3D mesh segmentation and labeling (2010, methodology): “…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; Li et al. 2008; Lai et al. 2008; Lavoué…”
A Gromov-Hausdorff Framework with Diffusion Geometry for Topologically-Robust Non-rigid Shape Matching (2010, methodology): “This method has been used in three-dimensional face recognition (Bronstein et al. 2005), analysis of articulated two-dimensional shapes and images (Ling and Jacobs 2005a, 2005b), texture mapping and object morphing (Bronstein et al. 2007a; Grossman et al. 2002 ;Z igelman et al.2002), and shape segme”
Model Composition from Interchangeable Components (2007, methodology): “While such segmentation has been mentioned as open problem by many researchers [21, 16 ], we are not familiar with any algorithms addressing it. In our system, we take a two step approach to computing compatible segmentations.”
Mesh Segmentation - A Comparative Study (2006, methodology): “The algorithm proposed in [14] produces hierarchical segmentations, with special emphasis on producing seg-”
Schelling points on 3D surface meshes (2012, methodology): “2009], and part-based segmentation [Katz et al. 2005; Zhou and Huang 2004].”
Direct visibility of point sets (2007, methodology): “Here, we focus on spherical flipping, which was first presented in [Katz et al. 2005] in a different context.”
A comprehensive survey of mostly textual document segmentation algorithms since 2008 (2017, background): “Scientific scope of this survey Segmentation algorithms can be applied to document images but also to a set of document images (in order to segment a book into its chapters by instance), to natural images [16], to medical images [17] and even to 3D meshes [18].”
Deepshape: Deep learned shape descriptor for 3D shape matching and retrieval (2015, background): “Nowadays there is an explosive growth of 3D meshed surface models in a variety of fields, such as engineering, entertainment and medical imaging [23, 20, 16, 10, 9, 6].”