Epsilon geometry: building robust algorithms from imprecise computations
SCG '89 Proceedings of the fifth annual symposium on Computational geometry
Triangulating a simple polygon in linear time
Discrete & Computational Geometry
Journal of Computer and System Sciences - Special issue: 31st IEEE conference on foundations of computer science, Oct. 22–24, 1990
Lower bounds for algebraic computation trees
STOC '83 Proceedings of the fifteenth annual ACM symposium on Theory of computing
Guarding scenes against invasive hypercubes
Computational Geometry: Theory and Applications
Almost-Delaunay simplices: nearest neighbor relations for imprecise points
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Efficient Update Strategies for Geometric Computing with Uncertainty
Theory of Computing Systems
Self-improving algorithms for delaunay triangulations
Proceedings of the twenty-fourth annual symposium on Computational geometry
Delaunay triangulations of imprecise pointsin linear time after preprocessing
Proceedings of the twenty-fourth annual symposium on Computational geometry
Efficient computation of continuous skeletons
SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
On Approximating the Depth and Related Problems
SIAM Journal on Computing
Triangulating input-constrained planar point sets
Information Processing Letters
Preprocessing Imprecise Points and Splitting Triangulations
ISAAC '08 Proceedings of the 19th International Symposium on Algorithms and Computation
Computing hereditary convex structures
Proceedings of the twenty-fifth annual symposium on Computational geometry
Self-improving algorithms for convex hulls
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
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Suppose we want to compute the Delaunay triangulation of a set P whose points are restricted to a collection ${\mathcal R}$ of input regions known in advance. Building on recent work by Löffler and Snoeyink[21], we show how to leverage our knowledge of ${\mathcal R}$ for faster Delaunay computation. Our approach needs no fancy machinery and optimally handles a wide variety of inputs, eg, overlapping disks of different sizes and fat regions.