Efficient and good Delaunay meshes from random points
Computer-Aided Design
SIAM Journal on Computing
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We present several results about Delaunay triangulations (DTs) and convex hulls in transdichotomous and hereditary settings: (i) the DT of a planar point set can be computed in expected time O(sort(n)) on a word RAM, where sort(n)is the time to sort n numbers. We assume that the word RAM supports the shuffle-operation in constant time; (ii) if we know the ordering of a planar point set in x- and in y-direction, its DT can be found by a randomized algebraic computation tree of expected linear depth;(iii) given a universe U of points in the plane, we construct a data structure D for Delaunay queries: for any subset P of U, D can find the DT of P in time O(|P| loglog |U|); (iv) given a universe U of points in 3-space in general convexposition, there is a data structure D for convex hull queries: for any subset P of U, D can find the convex hull of P in time O(|P| (log log |U|)^2);(v) given a convex polytope in 3-space with n vertices which are colored with k 2 colors, we can split it into the convex hulls of the individual color classes in time O(n (log log n)^2).The results (i)--(iii) generalize to higher dimensions. We need a wide range of techniques. Most prominently, we describe a reduction from DTs to nearest-neighbor graphs that relies on a new variant of randomized incremental constructions using dependent sampling.