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Re: [cgal-discuss] Convex Decomposition of Polyhedron inside CGAL or elsewhere?


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  • From: Peter Hachenberger <>
  • To:
  • Subject: Re: [cgal-discuss] Convex Decomposition of Polyhedron inside CGAL or elsewhere?
  • Date: Wed, 02 Apr 2008 14:05:07 +0200

Hi Maik,

I have exactly what you want, but it's not in CGAL, yet. It's working
nicely and is scheduled for the next CGAL release. I guess that is a bit
late for you.

Peter

On Tue, 2008-04-01 at 16:25 +0200, Maik Schulze wrote:
> Hello,
> I'm a student working on a computer game project at the university. I'm
> currently working on collision geometry. Computing the convex hulls of
> the point clouds of our vehicle models proved to be insufficient. And to
> keep things simple for the artists I thought the following might be the
> way to go.
>
> The artist builds a concave, closed polyhedron and runs a program that
> computes the convex decomposition of the polyhedron. The resulting
> convex polyhedra can then directly be handled by the physics engine. Or
> if the artist would like to do so, he can decrease the amount of the
> convex polyhedra by computing a common convex hull for two or more of
> them. For the last step, I can easily use CGAL functionality.
>
> The problem is the convex decomposition. Is there an algorithm or
> library that computes the convex decomposition for a closed polyhedron?
> Since it is not necessary to compute the optimal solution, I thought
> there might be something like the Greene's approximation for 3D? As such
> computations are only performed once on every model, the running time
> and memory consumption can be quite high. Meaning the program could use
> up to 4GB of RAM and run for 30 minutes on a current high-end consumer
> processor with an input polyhedron with 20.000 vertices.
>
> Is there such an algorithm, maybe even inside CGAL? Or could you provide
> me with some information on that matter? Unfortunately, Google wasn't
> very helpful on this issue for me. Please excuse my possible lack of
> knowledge about the theory behind this computational problem.
>
> Regards
> Maik Schulze




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