Hierarchical face clustering on polygonal surfaces

By Michael Garland, Andrew J Willmott, Paul S. Heckbert. ACM Symposium on Interactive 3D Graphics and Games, 2001

430 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/14935f4764d1c216fb8a5bbfa02599ace79a86ce

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Making papercraft toys from meshes using strip-based approximate unfolding (2004, methodology): “Methods for dividing a mesh model into parts have been proposed by several researchers (e.g. [Garland et al. 2001; Katz and Tal 2003; Lévy et al. 2002]); of these, we adopt the method of Lévy et al. Although this method was proposed for texture mapping, it is also good for papercraft – it detects…”
Mesh parameterization: theory and practice (2007, background): “planes are developable by definition, one possible approach is to segment the surface into nearly planar charts [Maillot et al., 1993; Garland et al., 2001; Sander et al., 2003; Cohen-Steiner et al., 2004].”
Mesh Segmentation - A Comparative Study (2006, methodology): “The algorithm has the same structure of the hierarchical face clustering proposed in [9], in which only fitting planes”
Hierarchical mesh segmentation based on fitting primitives (2006, methodology): “Thus, if one looks for a clustering algo-rithm to implement efﬁcient collision detection or multi-resolution radiosity, the original method described in [8] is sufﬁcient, while the extensions introduced in this paper are necessary to tackle problems such as the ones discussed in Sect.”
A contact searching algorithm including bounding volume trees applied to finite sliding mortar formulations (2007, methodology): “Another method to build the tree is the bottom-up approach, as exemplified by the surface clustering algorithm in [ 21 ].”
Mesh segmentation using feature point and core extraction (2005, methodology): “Some of these applications require segmentation into higher level meaningful components while others require segmentation into lower level disk-like patches [5, 10, 26, 34].”
Segmentation of 3D meshes through spectral clustering (2004, background): “Other mesh segmentation algorithms include convex decomposition [4], face merging in the context of mesh decimation [6], and skeletonization and space sweep [17].”
Multi-Chart Geometry Images (2003, methodology): “(Garland et al. [2001] would pro duce results comparable to (a).”
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].”
A planar-reflective symmetry transform for 3D shapes (2006, methodology): “However, instead of clustering based on a simplification [Garland et al. 2001] or based on geodesic and angular distance between points [Katz and Tal 2003], we cluster based on support for local maxima in the PRST.”
Interactive geometry remeshing (2002, methodology): “From left to right: geometry of a Bunny ear; conformal parameterization and resulting area distortion visualized through a texture mapping of a checkerboard; face clustering obtained using [15]; partitioning obtained by simple bisection [11, 21]; the conformal parameterization, with the two medians;”
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].”