Feature sensitive surface extraction from volume data

By Leif Kobbelt, Mario Botsch, Ulrich Schwanecke, Hans-Peter Seidel. International Conference on Computer Graphics and Interactive Techniques, 2001

598 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/d31183e6a451f673104aee750d60a341024d4fbc

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Spatially adaptive techniques for level set methods and incompressible flow (2006, background): “A traditional drawback of implicit functions in regard to constructive solid geometry and surface editing is the inability to represent fine or sharp features, and the surface reconstruction technique of [51] attempts to identify these features and insert the appropriate geometry.”
3D quantitative shape analysis on form, roundness, and compactness with μCT (2016, unknown): “, Laplacian iterative smoothing [36] and feature-sensitive surface extraction [37].”
Robust moving least-squares fitting with sharp features (2005, background): “Recent advances in contouring techniques reconstruct sharp features [Ju et al. 2002; Kobbelt et al. 2001; Varadhan et al. 2003].”
Dual contouring of hermite data (2002, methodology): “To solve this problem, we compute the SVD decomposition of Â and form its pseudo-inverse by truncating its small singular values as done in [Kobbelt et al. 2001; Lindstrom 2000].”
Robust repair of polygonal models (2004, methodology): “Contouring algorithms can be classified into two types: primal methods (such as Marching Cubes [Lorensen and Cline 1987]) and hybrid methods (such as the Feature Sensitive Surface Extraction method [Kobbelt et al. 2001] and Dual Contouring method [Ju et al. 2002]).”
Three-dimensional sphericity, roundness and fractal dimension of sand particles (2018, methodology): “To reconstruct the particle surface, a variety of smoothing algorithms (Field, 1988; Kobbelt et al., 2001) have been proposed to simultaneously remove the stair-steps and preserve the overall particle morphology.”
Recent Advances in Remeshing of Surfaces (2008, methodology): “To cope with this problem, an extended marching cubes algorithm was proposed in [44].”
3D distance fields: a survey of techniques and applications (2006, background): “Further on, the authors propose a unified DF (UDF), which combines the aforementioned offset DF with representations proposed in [63], [53], [57], together with the plain DF storing just the minimal distances.”
A survey of the marching cubes algorithm (2006, background): “One way to reduce such artifacts is to use additional sample points in the vicinity of sharp edges and corners and then generate a triangle fan about each additional point [135].”
A survey of point-based techniques in computer graphics (2004, background): “Additionally, alias artifacts are introduced by insufficient sampling, which cannot be removed by increasing the sampling density [18].”
Multi‐scale Feature Extraction on Point‐Sampled Surfaces (2003, background): “Feature sensitive meshing of surfaces defined as the zeroset of a discrete 3D distance function has been presented in [13].”