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82
DavenportSchinzel Sequences and Their Geometric Applications
, 1998
"... An (n; s) DavenportSchinzel sequence, for positive integers n and s, is a sequence composed of n distinct symbols with the properties that no two adjacent elements are equal, and that it does not contain, as a (possibly noncontiguous) subsequence, any alternation a \Delta \Delta \Delta b \Delta \ ..."
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Cited by 416 (115 self)
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An (n; s) DavenportSchinzel sequence, for positive integers n and s, is a sequence composed of n distinct symbols with the properties that no two adjacent elements are equal, and that it does not contain, as a (possibly noncontiguous) subsequence, any alternation a \Delta \Delta \Delta b \Delta \Delta \Delta a \Delta \Delta \Delta b \Delta \Delta \Delta of length s + 2 between two distinct symbols a and b. The close relationship between DavenportSchinzel sequences and the combinatorial structure of lower envelopes of collections of functions make the sequences very attractive because a variety of geometric problems can be formulated in terms of lower envelopes. A nearlinear bound on the maximum length of DavenportSchinzel sequences enable us to derive sharp bounds on the combinatorial structure underlying various geometric problems, which in turn yields efficient algorithms for these problems.
Efficient Binary Space Partitions for HiddenSurface Removal and Solid Modeling
, 1990
"... We consider schemes for recursively dividing a set of geometric objects by hyperplanes until all objects are separated. Such a binary space partition, or BSP, is naturally considered as a binary tree where each internal node corresponds to a division. The goal is to choose the hyperplanes properly ..."
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Cited by 91 (0 self)
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We consider schemes for recursively dividing a set of geometric objects by hyperplanes until all objects are separated. Such a binary space partition, or BSP, is naturally considered as a binary tree where each internal node corresponds to a division. The goal is to choose the hyperplanes properly so that the size of the BSP, i.e., the number of resulting fragments of the objects, is minimized. For the twodimensional case, we construct BSPs of size O(n log n) for n edges, while in three dimensions, we obtain BSPs of size O(n²) for n planar facets and prove a matching lower bound of Ω(n²). Two applications of efficient BSPs are given. The first is an O(n²)sized data structure for implementing a hiddensurface removal scheme of Fuchs et al. [6]. The second application is in solid modeling: given a polyhedron described by its n faces, we show how to generate an O(n²)sized CSG (constructivesolidgeometry) formula whose literals correspond to halfspaces supporting the faces of the polyhedron. The best previous results for both of these problems were O(n³).
Applications of parametric searching in geometric optimization
 J. Algorithms
, 1994
"... z Sivan Toledo x ..."
New Bounds for Lower Envelopes in Three Dimensions, with Applications to Visibility in Terrains
 Geom
, 1997
"... We consider the problem of bounding the complexity of the lower envelope of n surface patches in 3space, all algebraic of constant maximum degree, and bounded by algebraic arcs of constant maximum degree, with the additional property that the interiors of any triple of these surfaces intersect i ..."
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Cited by 59 (25 self)
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We consider the problem of bounding the complexity of the lower envelope of n surface patches in 3space, all algebraic of constant maximum degree, and bounded by algebraic arcs of constant maximum degree, with the additional property that the interiors of any triple of these surfaces intersect in at most two points. We show that the number of vertices on the lower envelope of n such surface patches is O(n 2 \Delta 2 c p log n ), for some constant c depending on the shape and degree of the surface patches. We apply this result to obtain an upper bound on the combinatorial complexity of the `lower envelope' of the space of all rays in 3space that lie above a given polyhedral terrain K with n edges. This envelope consists of all rays that touch the terrain (but otherwise lie above it). We show that the combinatorial complexity of this rayenvelope is O(n 3 \Delta 2 c p log n ) for some constant c; in particular, there are at most that many rays that pass above th...
MinimumLink Paths Among Obstacles in the Plane
 ALGORITHMICA
, 1992
"... Given a set of nonintersecting polygonal obstacles in the plane, the link distance between two points s and t is the minimum number of edges required to form a polygonal path connecting s to t that avoids all obstacles. We present an algorithm that computes the link distance (and a correspon ..."
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Cited by 53 (6 self)
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Given a set of nonintersecting polygonal obstacles in the plane, the link distance between two points s and t is the minimum number of edges required to form a polygonal path connecting s to t that avoids all obstacles. We present an algorithm that computes the link distance (and a corresponding minimumlink path) between two points in time O(E#(n) log² n) (and space O(E)), where n is the total number of edges of the obstacles, E is the size of the visibility graph, and #(n) denotes the extremely slowly growing inverse of Ackermann's function. We show how to extend our method to allow computation of a tree (rooted at s) of minimumlink paths from s to all obstacle vertices. This leads to a method of solving the query version of our problem (for query points t).
Vertical decomposition of shallow levels in 3dimensional arrangements and its applications
 SIAM J. Comput
"... Let F be a collection of n bivariate algebraic functions of constant maximum degree. We show that the combinatorial complexity of the vertical decomposition of the ≤klevel of the arrangement A(F) is O(k 3+ε ψ(n/k)), for any ε> 0, where ψ(r) is the maximum complexity of the lower envelope of a subse ..."
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Cited by 53 (13 self)
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Let F be a collection of n bivariate algebraic functions of constant maximum degree. We show that the combinatorial complexity of the vertical decomposition of the ≤klevel of the arrangement A(F) is O(k 3+ε ψ(n/k)), for any ε> 0, where ψ(r) is the maximum complexity of the lower envelope of a subset of at most r functions of F. This bound is nearly optimal in the worst case, and implies the existence of shallow cuttings, in the sense of [52], of small size in arrangements of bivariate algebraic functions. We also present numerous applications of these results, including: (i) data structures for several generalized threedimensional rangesearching problems; (ii) dynamic data structures for planar nearest and farthestneighbor searching under various fairly general distance functions; (iii) an improved (nearquadratic) algorithm for minimumweight bipartite Euclidean matching in the plane; and (iv) efficient algorithms for certain geometric optimization problems in static and dynamic settings.
Voronoi Diagrams of Moving Points
, 1995
"... Consider a set of n points in ddimensional Euclidean space, d 2, each of which is continuously moving along a given individual trajectory. At each instant in time, the points define a Voronoi diagram. As the points move, the Voronoi diagram changes continuously, but at certain critical instants in ..."
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Cited by 46 (6 self)
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Consider a set of n points in ddimensional Euclidean space, d 2, each of which is continuously moving along a given individual trajectory. At each instant in time, the points define a Voronoi diagram. As the points move, the Voronoi diagram changes continuously, but at certain critical instants in time, topological events occur that cause a change in the Voronoi diagram. In this paper, we present a method of maintaining the Voronoi diagram over time, while showing that the number of topological events has an upper bound of O(n d s (n)), where s (n) is the maximum length of a (n; s)DavenportSchinzel sequence [AgShSh 89, DaSc 65] and s is a constant depending on the motions of the point sites. Our results are a linearfactor improvement over the naive O(n d+2 ) upper bound on the number of topological events. In addition, we show that if only k points are moving (while leaving the other n \Gamma k points fixed), there is an upper bound of O(kn d\Gamma1 s (n) + (n \Gamma k)...
Optimal OutputSensitive Convex Hull Algorithms in Two and Three Dimensions
, 1996
"... We present simple outputsensitive algorithms that construct the convex hull of a set of n points in two or three dimensions in worstcase optimal O(n log h) time and O(n) space, where h denotes the number of vertices of the convex hull. ..."
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Cited by 45 (6 self)
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We present simple outputsensitive algorithms that construct the convex hull of a set of n points in two or three dimensions in worstcase optimal O(n log h) time and O(n) space, where h denotes the number of vertices of the convex hull.
On Dynamic Voronoi Diagrams and the Minimum Hausdorff Distance for Point Sets Under Euclidean Motion in the Plane
, 1992
"... We show that the dynamic Voronoi diagram of k sets of points in the plane, where each set consists of n points moving rigidly, has complexity O(n 2 k 2 s (k)) for some fixed s, where s (n) is the maximum length of a (n; s) DavenportSchinzel sequence. This improves the result of Aonuma et. al ..."
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Cited by 45 (3 self)
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We show that the dynamic Voronoi diagram of k sets of points in the plane, where each set consists of n points moving rigidly, has complexity O(n 2 k 2 s (k)) for some fixed s, where s (n) is the maximum length of a (n; s) DavenportSchinzel sequence. This improves the result of Aonuma et. al., who show an upper bound of O(n 3 k 4 log k) for the complexity of such Voronoi diagrams. We then apply this result to the problem of finding the minimum Hausdorff distance between two point sets in the plane under Euclidean motion. We show that this distance can be computed in time O((m + n) 6 log(mn)), where the two sets contain m and n points respectively. This work was supported in part by NSF grant IRI9057928 and matching funds from General Electric and Kodak, and in part by AFOSR under contract AFOSR910328. The second author was also supported by the Eshkol grant 0460190 from The Israeli Ministry of Science and Technology. 1. Introduction Determining the degree to ...
New Methods for Computing Visibility Graphs
, 1988
"... Let S be a set of n nonintersecting line segments in the plane. The visibility graph Gs of S is the graph that has the endpoints of the segments in S as nodes and in which two nodes are adjacent whenever they can "see"each other (i.e., the open line segment join ing them is disjoint from all segme ..."
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Cited by 35 (2 self)
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Let S be a set of n nonintersecting line segments in the plane. The visibility graph Gs of S is the graph that has the endpoints of the segments in S as nodes and in which two nodes are adjacent whenever they can "see"each other (i.e., the open line segment join ing them is disjoint from all segments or is contained in a segment). Two new methods are presented to construct Gs. Both methods are very simple to implement. The first method is based on a new solution to the following problem: given a set of points, for each point sort the other points around it by angle. It runs in time O(n2). The second method uses the fact that visibility graphs often are sparse and runs in time O(m log n) where m is the number of edges in Gs. Both methods use only O(n) storage.