478 research outputs found
Data Structures for Halfplane Proximity Queries and Incremental Voronoi Diagrams
We consider preprocessing a set of points in convex position in the
plane into a data structure supporting queries of the following form: given a
point and a directed line in the plane, report the point of that
is farthest from (or, alternatively, nearest to) the point among all points
to the left of line . We present two data structures for this problem.
The first data structure uses space and preprocessing
time, and answers queries in time, for any . The second data structure uses space and
polynomial preprocessing time, and answers queries in time. These
are the first solutions to the problem with query time and
space.
The second data structure uses a new representation of nearest- and
farthest-point Voronoi diagrams of points in convex position. This
representation supports the insertion of new points in clockwise order using
only amortized pointer changes, in addition to -time
point-location queries, even though every such update may make
combinatorial changes to the Voronoi diagram. This data structure is the first
demonstration that deterministically and incrementally constructed Voronoi
diagrams can be maintained in amortized pointer changes per operation
while keeping -time point-location queries.Comment: 17 pages, 6 figures. Various small improvements. To appear in
Algorithmic
A minimalistic approach for fast computation of geodesic distances on triangular meshes
The computation of geodesic distances is an important research topic in
Geometry Processing and 3D Shape Analysis as it is a basic component of many
methods used in these areas. In this work, we present a minimalistic parallel
algorithm based on front propagation to compute approximate geodesic distances
on meshes. Our method is practical and simple to implement and does not require
any heavy pre-processing. The convergence of our algorithm depends on the
number of discrete level sets around the source points from which distance
information propagates. To appropriately implement our method on GPUs taking
into account memory coalescence problems, we take advantage of a graph
representation based on a breadth-first search traversal that works
harmoniously with our parallel front propagation approach. We report
experiments that show how our method scales with the size of the problem. We
compare the mean error and processing time obtained by our method with such
measures computed using other methods. Our method produces results in
competitive times with almost the same accuracy, especially for large meshes.
We also demonstrate its use for solving two classical geometry processing
problems: the regular sampling problem and the Voronoi tessellation on meshes.Comment: Preprint submitted to Computers & Graphic
Proximity problems on line segments spanned by points
AbstractFinding the closest or farthest line segment (line) from a point are fundamental proximity problems. Given a set S of n points in the plane and another point q, we present optimal O(nlogn) time, O(n) space algorithms for finding the closest and farthest line segments (lines) from q among those spanned by the points in S. We further show how to apply our techniques to find the minimum (maximum) area triangle with a vertex at q and the other two vertices in S∖{q} in optimal O(nlogn) time and O(n) space. Finally, we give an O(nlogn) time, O(n) space algorithm to find the kth closest line from q and show how to find the k closest lines from q in O(nlogn+k) time and O(n+k) space
Dispersion of Mass and the Complexity of Randomized Geometric Algorithms
How much can randomness help computation? Motivated by this general question
and by volume computation, one of the few instances where randomness provably
helps, we analyze a notion of dispersion and connect it to asymptotic convex
geometry. We obtain a nearly quadratic lower bound on the complexity of
randomized volume algorithms for convex bodies in R^n (the current best
algorithm has complexity roughly n^4, conjectured to be n^3). Our main tools,
dispersion of random determinants and dispersion of the length of a random
point from a convex body, are of independent interest and applicable more
generally; in particular, the latter is closely related to the variance
hypothesis from convex geometry. This geometric dispersion also leads to lower
bounds for matrix problems and property testing.Comment: Full version of L. Rademacher, S. Vempala: Dispersion of Mass and the
Complexity of Randomized Geometric Algorithms. Proc. 47th IEEE Annual Symp.
on Found. of Comp. Sci. (2006). A version of it to appear in Advances in
Mathematic
Moving Walkways, Escalators, and Elevators
We study a simple geometric model of transportation facility that consists of
two points between which the travel speed is high. This elementary definition
can model shuttle services, tunnels, bridges, teleportation devices, escalators
or moving walkways. The travel time between a pair of points is defined as a
time distance, in such a way that a customer uses the transportation facility
only if it is helpful.
We give algorithms for finding the optimal location of such a transportation
facility, where optimality is defined with respect to the maximum travel time
between two points in a given set.Comment: 16 pages. Presented at XII Encuentros de Geometria Computacional,
Valladolid, Spai
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