Surface simplification using quadric error metrics

By Michael Garland, Paul S. Heckbert. 1997

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Citations in the corpus

Generating 3D faces using Convolutional Mesh Autoencoders (2018, methodology): “Mesh Sampling Operations: A mesh feature (a) is down-sampled by removing red vertices that minimize quadric error [20].”
SCAPE: shape completion and animation of people (2005, methodology): “We used this scanner to construct full-body instance meshes by merging the four scan views [Curless and Levoy 1996] and subsampling the instances to about 50,000 triangles [Garland and Heckbert 1997].”
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence (2017, methodology): “For increased efficiency, we down-sample the input shapes to 15K vertices by edge contraction [19]; in case the input mesh has a smaller amount of vertices, it is kept at full resolution.”
Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids (2006, methodology): “The simplified meshes could be quickly obtained with the program QSlim (using a method described in Garland [31]).”
Surface Simpliﬁcation Using Quadric Error Metrics (2018, methodology): “Michael Garland and Paul S. Heckbert propose a surface simpliﬁcation algorithm that uses their Quadric Error Metric to choose vertices for iterative pair contraction [3].”
1 An End-to-End Framework for Evaluating Surface Reconstruction (2011, methodology): “Quadric error functions [Garland and Heckbert 1997] are used to position points”
Fully automatic registration of multiple 3D data sets (2003, methodology): “For computational efficiency, the meshes are simplified using Garland’s quadric algorithm [20].”
Multiresolution signal processing for meshes (1999, methodology): “As a priority criterion we use a combination of the GarlandHeckbert quadric error metric [11] and edge length to favor removal of long edges (see also [17]).”
Stylized rendering techniques for scalable real-time 3D animation (2000, methodology): “Our Multi-Resolution Mesh (MRM) system is a plug-in for a 3D authoring tool based on the work by Garland and Heckbert [ Garl97 ].”
Simplifying surfaces with color and texture using quadric error metrics (1998, methodology): “Iterative edge contraction [6, 7, 8, 15, 16] has been widely used.”
Feature Extraction From Point Clouds (2001, methodology): “The tool to solve this minimization problem is also known as quadric error metrics, orignally introduced by Garland [9] in mesh simplification to find optimal locations for the mesh vertice after an edge collapse operation.”
Geometric modeling in shape space (2007, methodology): “Combining methods from [Hoppe 1996] and [Garland and Heckbert 1997], the input meshes are concurrently decimated to preserve correspondences across all resolutions of the resulting progressive meshes.”
Skeleton extraction by mesh contraction (2008, methodology): “The shape cost term is conceptually similar to the popular QEM simplification method [Garland and Heckbert 1997] which preserves mesh geometry well.”