ABSTRACT
We describe a new algorithm for computing the Voronoi diagram of a set of n points in constant-dimensional Euclidean space. The running time of our algorithm is O(f log n log Δ) where f is the output complexity of the Voronoi diagram and Δ is the spread of the input, the ratio of largest to smallest pairwise distances. Despite the simplicity of the algorithm and its analysis, it improves on the state of the art for all inputs with polynomial spread and near-linear output size. The key idea is to first build the Voronoi diagram of a superset of the input points using ideas from Voronoi refinement mesh generation. Then, the extra points are removed in a straightforward way that allows the total work to be bounded in terms of the output complexity, yielding the output sensitive bound. The removal only involves local flips and is inspired by kinetic data structures.
- Umut A. Acar, Benoıt Hudson, Gary L. Miller, and Todd Phillips. SVR: Practical engineering of a fast 3D meshing algorithm. In Proc. 16th International Meshing Roundtable, pages 45--62, 2007.Google Scholar
- Nancy M. Amato and Edgar A. Ramos. On computing Voronoi diagrams by divide-prune-and-conquer. In Symposium on Computational Geometry, pages 166--175, 1996. Google Scholar
Digital Library
- Franz Aurenhammer. Voronoi diagrams--a survey of a fundamental geometric data structure. ACM Comput. Surv., 23(3):345--405, September 1991. Google Scholar
Digital Library
- David Avis and Komei Fukuda. A pivoting algorithm for convex hulls and vertex enumeration of arrangements and polyhedra. Discrete & Computational Geometry, 8(1):295--313, 1992.Google Scholar
Digital Library
- David Bremner. Incremental convex hull algorithms are not output sensitive. Discrete & Computational Geometry, 21(1):57--68, 1999.Google Scholar
Cross Ref
- Timothy M. Chan. Output-sensitive results on convex hulls, extreme points, and related problems. Discrete & Computational Geometry, 16(4):369--387, 1996.Google Scholar
Digital Library
- Timothy M. Chan, Jack Snoeyink, and Chee-Keng Yap. Primal dividing and dual pruning: Output-sensitive construction of four-dimensional polytopes and three-dimensional Voronoi diagrams. Discrete & Computational Geometry, 18(4):433--454, 1997.Google Scholar
Cross Ref
- Bernard Chazelle, Olivier Devillers, Ferran Hurtado, Mercè Mora, Vera Sacristán, and Monique Teillaud. Splitting a Delaunay triangulation in linear time. Algorithmica, 34:39--46, 2002.Google Scholar
Digital Library
- Bernard Chazelle and Wolfgang Mulzer. Computing hereditary convex structures. Discrete & Computational Geometry, 45(4):796--823, 2011. Google Scholar
Digital Library
- Siu-Wing Cheng, Tamal K. Dey, and Jonathan Richard Shewchuk. Delaunay Mesh Generation. CRC Press, 2012. Google Scholar
Digital Library
- Herbert Edelsbrunner. Geometry and Topology for Mesh Generation. Cambridge University Press, 2001. Google Scholar
Digital Library
- Herbert Edelsbrunner and Nimish R. Shah. Incremental topological flipping works for regular triangulations. Algorithmica, 15, 1996.Google Scholar
- Leonidas Guibas. Kinetic data structures. In Dinesh P. Mehta and Sartaj Sahni, editors, Handbook of Data Structures and Applications. CRC Press, 2005.Google Scholar
- Sariel Har-Peled and Manor Mendel. Fast construction of nets in low dimensional metrics, and their applications. SIAM Journal on Computing, 35(5):1148--1184, 2006. Google Scholar
Digital Library
- Benoıt Hudson, Gary Miller, and Todd Phillips. Sparse Voronoi Refinement. In Proceedings of the 15th International Meshing Roundtable, pages 339--356, Birmingham, Alabama, 2006. Long version available as Carnegie Mellon University Technical Report Carnegie Mellon University-CS-06--132.Google Scholar
Cross Ref
- Benoıt Hudson, Gary L. Miller, and Todd Phillips. Sparse Parallel Delaunay Refinement. In 19th Annual ACM Symposium on Parallelism in Algorithms and Architectures, pages 339--347, San Diego, June 2007. Google Scholar
Digital Library
- Benoıt Hudson, Gary L. Miller, Todd Phillips, and Donald R. Sheehy. Size complexity of volume meshes vs. surface meshes. In SODA: ACM-SIAM Symposium on Discrete Algorithms, 2009. Google Scholar
Digital Library
- Michael Joswig and Günter M. Ziegler. Convex hulls, oracles, and homology. J. Symb. Comput, 38(4):1247--1259, 2004.Google Scholar
Cross Ref
- V. Klee. On the complexity of d-dimensional Voronoi diagrams. Archiv der Mathematik, 34:75, 1980.Google Scholar
Cross Ref
- Jir'ı Matousek and Otfried Schwarzkopf. Linear optimization queries. In Symposium on Computational Geometry, pages 16--25, 1992. Google Scholar
Digital Library
- Gary L. Miller, Todd Phillips, and Donald R. Sheehy. Beating the spread: Time-optimal point meshing. In SOCG: Proceedings of the 27th ACM Symposium on Computational Geometry, pages 321--330, 2011. Google Scholar
Digital Library
- Gary L. Miller, Donald R. Sheehy, and Ameya Velingker. A fast algorithm for well-spaced points and approximate delaunay graphs. In SOCG: Proceedings of the 29th ACM Symposium on Computational Geometry, 2013. Google Scholar
Digital Library
- Gary L. Miller, Dafna Talmor, Shang-Hua Teng, and Noel Walkington. On the radius-edge condition in the control volume method. SIAM J. on Numerical Analysis, 36(6):1690--1708, 1999. Google Scholar
Digital Library
- Raimund Seidel. Constructing higher-dimensional convex hulls at logarithmic cost per face. In STOC: ACM Symposium on Theory of Computing, 1986. Google Scholar
Digital Library
- Raimund Seidel. On the number of faces in higher-dimensional Voronoi diagrams. In Proceedings of the 3rd Annual Symposium on Computational Geometry, pages 181--185, 1987. Google Scholar
Digital Library
- Donald R. Sheehy. Mesh Generation and Geometric Persistent Homology. PhD thesis, Carnegie Mellon University, 2011. Google Scholar
Digital Library
- Donald R. Sheehy. New Bounds on the Size of Optimal Meshes. Computer Graphics Forum, 31(5):1627--1635, 2012. Google Scholar
Digital Library
- Garret Swart. Finding the convex hull facet by facet. Journal of Algorithms, 6(1):17--48, 1985.Google Scholar
Cross Ref
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A new approach to output-sensitive voronoi diagrams and delaunay triangulations
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