48 lines
2.1 KiB
Plaintext
48 lines
2.1 KiB
Plaintext
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Qhull 2015.2 2016/01/18
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http://www.qhull.org
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git@github.com:qhull/qhull.git
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http://www.geomview.org
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Qhull computes convex hulls, Delaunay triangulations, Voronoi diagrams,
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furthest-site Voronoi diagrams, and halfspace intersections about a point.
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It runs in 2-d, 3-d, 4-d, or higher. It implements the Quickhull algorithm
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for computing convex hulls. Qhull handles round-off errors from floating
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point arithmetic. It can approximate a convex hull.
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The program includes options for hull volume, facet area, partial hulls,
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input transformations, randomization, tracing, multiple output formats, and
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execution statistics. The program can be called from within your application.
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You can view the results in 2-d, 3-d and 4-d with Geomview.
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To download Qhull:
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http://www.qhull.org/download
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git@github.com:qhull/qhull.git
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Download qhull-96.ps for:
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Barber, C. B., D.P. Dobkin, and H.T. Huhdanpaa, "The
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Quickhull Algorithm for Convex Hulls," ACM Trans. on
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Mathematical Software, 22(4):469-483, Dec. 1996.
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http://www.acm.org/pubs/citations/journals/toms/1996-22-4/p469-barber/
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.117.405
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Abstract:
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The convex hull of a set of points is the smallest convex set that contains
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the points. This article presents a practical convex hull algorithm that
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combines the two-dimensional Quickhull Algorithm with the general dimension
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Beneath-Beyond Algorithm. It is similar to the randomized, incremental
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algorithms for convex hull and Delaunay triangulation. We provide empirical
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evidence that the algorithm runs faster when the input contains non-extreme
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points, and that it uses less memory.
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Computational geometry algorithms have traditionally assumed that input sets
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are well behaved. When an algorithm is implemented with floating point
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arithmetic, this assumption can lead to serious errors. We briefly describe
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a solution to this problem when computing the convex hull in two, three, or
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four dimensions. The output is a set of "thick" facets that contain all
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possible exact convex hulls of the input. A variation is effective in five
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or more dimensions.
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