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Sparse Meshing

SVR: provably fast mesh refinement. The SVR algorithm is the first Delaunay refinement algorithm to be provably fast, but still produce the small meshes associated with Delaunay refinement. This page describes and distributes an implementation of SVR, which we have shown is faster than the TetGen or Pyramid codes when computing quality meshes of point-cloud inputs. What is SVR good for? You will not find a mode for computing the Delaunay triangulation of an input without refining it, because this would o...

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Carnegie Mellon University

Benoit Hudson

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Pit●●●rgh , PA, 15213

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bh●●●●●@cs.cmu.edu

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Sparse Meshing | sparse-meshing.com Reviews
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SVR: provably fast mesh refinement. The SVR algorithm is the first Delaunay refinement algorithm to be provably fast, but still produce the small meshes associated with Delaunay refinement. This page describes and distributes an implementation of SVR, which we have shown is faster than the TetGen or Pyramid codes when computing quality meshes of point-cloud inputs. What is SVR good for? You will not find a mode for computing the Delaunay triangulation of an input without refining it, because this would o...
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2 running svr
3 publications
4 api documentation
5 and rhoprime
6 maturity
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Sparse Meshing | sparse-meshing.com Reviews

https://sparse-meshing.com

SVR: provably fast mesh refinement. The SVR algorithm is the first Delaunay refinement algorithm to be provably fast, but still produce the small meshes associated with Delaunay refinement. This page describes and distributes an implementation of SVR, which we have shown is faster than the TetGen or Pyramid codes when computing quality meshes of point-cloud inputs. What is SVR good for? You will not find a mode for computing the Delaunay triangulation of an input without refining it, because this would o...

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1

Sparse Meshing: .node format

http://www.sparse-meshing.com/svr/0.2.1/format-node.html

Format devised by Shewchuk, generalized in the obvious way to higher dimension, to describe sets of vertices in both input and output. The format is a text file, consisting of a header describing the file, then a list of points. Empty lines are ignored, as is anything on the same line after a '#' comment character. The amount or type of whitespace is insignificant. Here is an example file:. 6 2 0 0 1 0 0 2 0 1 3 1 0 4 1 1 5 0.5 0.5 6 0.6 0.7. N d nattr nbdry. Id x y [z, .]. Back to SVR home.

2

Sparse Meshing: known bugs in SVR

http://www.sparse-meshing.com/svr/0.2.1/bugs.html

Known bugs in SVR. Points within distance about 1e-6 of each other can cause numerical issues and crashes. The problem is presumed to be that intermediate calculations use denormalized floating-point numbers. The proper fix requires fixing the geometric predicates and circumcenter computations to still work in the face of denormalization. The easiest workaround is to rescale all the points before handing them to SVR. Sliver elimination on point clouds does not always terminate. This is a fundamental issu...

3

Main Page

http://www.sparse-meshing.com/svr/0.2.1/doxygen/html/index.html

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Sparse Meshing: papers

http://www.sparse-meshing.com/svr/0.2.1/papers.html

People and papers related to SVR. The main article describing the theoretical algorithm is Hudson, Miller, Phillips, 2006. Sparse Voronoi Refinement. The main article describing the code and experiments is Acar, Hudson, Miller, Phillips, 2007. SVR: Practical Engineering for a Fast 3D Meshing Algorithm. The experimental results are reprised in some greater detail in chapters 2 and 3 of the Ph.D. thesis Hudson, 2007. Dynamic Mesh Refinement. The following people have worked on SVR:. Umut A. Acar. Optimal-t...

5

Benoit Hudson

http://www.sparse-meshing.com/bhudson

I am the founder and CEO of Imaginary Spaces. We help furniture retailers connect with their customers. Previously I was Senior Software Developer at Autodesk. Working in the Media and Entertainment Consulting group until November 2012. Before that I was a Research Assistant Professor at TTI-C. I maintain sparse-meshing.com. In December 2007 I completed my Ph.D. at CMU. In the computer science department, working with Gary Miller. Prior to this I worked at NASA Ames Research Center. Points whose trajecto...

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Benoit Hudson

http://ttic.uchicago.edu/~bhudson

I am a Research Assistant Professor at TTI-C. I maintain sparse-meshing.com. In December 2007 I completed my Ph.D. at CMU. In the computer science department, working with Gary Miller. On computational geometry and scientific computing. My first year and a half there I was working with Tuomas Sandholm. Prior to this I worked at NASA Ames Research Center. In the model-based autonomy group, most notably working on the L2. Dynamic Well-Spaced Point Sets. Topological Inference via Meshing. My FWCG paper from...

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SVR: provably fast mesh refinement. The SVR algorithm is the first Delaunay refinement algorithm to be provably fast, but still produce the small meshes associated with Delaunay refinement. This page describes and distributes an implementation of SVR, which we have shown is faster than the TetGen or Pyramid codes when computing quality meshes of point-cloud inputs. What is SVR good for? You will not find a mode for computing the Delaunay triangulation of an input without refining it, because this would o...

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