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A fast algorithm for simulating particle repulsion on surfaces is described. Particles are distributed across a surface and made to repel. Calculating pairwise repulsion between n particles costs O(n 2 ) in general, but if the energy potential drops off very rapidly, repulsion can be simulated in O(n) time. This is accomplished using a simple grid data structure with spherical range queries. Particles change in number and position, necessitating a dynamic data structure. The method is used to accelerate repulsion in an algorithm for uniformly sampling implicit surfaces during interactive surface design. Although this method is straightforward, it is quite effective: speedups of 160 times were realized for simulations with 10,000 particles. We also give references to related work outside the field of computer graphics. Keywords: physically based modeling, spatial data structure, grid, spherical range search, bucketing. 1 Introduction To display surfaces rapidly during interactive...
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A fast algorithm for simulating particle repulsion on surfaces is described. Particles are distributed across a surface and made to repel. Calculating pairwise repulsion between n particles costs O(n 2 ) in general, but if the energy potential drops off very rapidly, repulsion can be simulated in O(n) time. This is accomplished using a simple grid data structure with spherical range queries. Particles change in number and position, necessitating a dynamic data structure. The method is used to accelerate repulsion in an algorithm for uniformly sampling implicit surfaces during interactive surface design. Although this method is straightforward, it is quite effective: speedups of 160 times were realized for simulations with 10,000 particles. We also give references to related work outside the field of computer graphics. Keywords: physically based modeling, spatial data structure, grid, spherical range search, bucketing. 1 Introduction To display surfaces rapidly during interactive...
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