Efficient simplification of point-sampled surfaces

By Mark Pauly, Markus H. Gross, Leif Kobbelt. IEEE Visualization, 2002. VIS 2002., 2002

1,097 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e64dfa02c5d5e67a9adff6b1aafad258b13ec8e7

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

A Closed-Form Expression of the Positional Uncertainty for 3D Point Clouds (2009, methodology): “We implemented Pauly et al.’s [ 25 ] curvature-adaptive clustering method with the modification that the centroid of a cluster was weighted by the curvature of the members of the cluster and the angular variance in the estimation of the surface normal vector was used.”
Consolidation of unorganized point clouds for surface reconstruction (2009, methodology): “First, to make local PCA more robust, we denoise, remove outliers, and down-sample the otherwise dense input point cloud to obtain a thinned and evenly distributed set of points, called particles [Pauly et al. 2002] to distinguish from the input points.”
A survey of point-based techniques in computer graphics (2004, methodology): “ Pauly et al. (2002) transfered several mesh decimation methods — vertex clustering, incremental decimation using error quadrics, and remeshing by particle simulation — to point-sampled surfaces.”
CLASSIFICATION AND SEGMENTATION OF TERRESTRIAL LASER SCANNER POINT CLOUDS USING LOCAL VARIANCE INFORMATION (2006, methodology): “Covariance analysis is often used as a starting point in classiﬁ-cation of 3D point clouds based on geometric properties (Pauly et al. , 2002).”
A Theoretical and Computational Framework for Isometry Invariant Recognition of Point Cloud Data (2005, background): “See for example [5, 15, 18, 21, 27, 39, 40, 48, 49] for a few of the recent works with this type of data.”
Shape modeling with point-sampled geometry (2003, methodology): “the standard MLS method as proposed in [Pauly et al. 2002], which is based on sampling density estimation.”
Point based animation of elastic, plastic and melting objects (2004, methodology): “To achieve this and to spread the surfels over uncovered surface areas in the case of topological changes, a repulsion scheme is applied to the surfels, similar to [ PGK02 ].”
NICP: Dense normal based point cloud registration (2015, background): “curvature σi = λ1 / (λ1 +λ2 +λ3) to discriminate how well the surface is fitted by a plane (see [14] for more details).”
Towards 3D Point cloud based object maps for household environments (2008, methodology): “The estimated normal n of the point p can be approximated with the normal to the k-neighborhood surface by performing PCA (Principal Component Analysis) on the neighborhood’s covariance matrix [25].”
Implicit Functions in Feature Space for 3D Shape Reconstruction and Completion (2020, background): “Due to their low weight, they have been also popular in computer graphics for representing and manipulating shapes [50].”