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Efficient computation and simplification of discrete morse decompositions on triangulated terrains

Published:04 November 2014Publication History

ABSTRACT

We consider the problem of efficient computing and simplifying Morse complexes on a Triangulated Irregular Network (TIN) based on discrete Morse theory. We develop a compact encoding for the discrete Morse gradient field, defined by the terrain elevation, by attaching it to the triangles of the TIN. This encoding is suitable to be combined with any TIN data structure storing just its vertices and triangles. We show how to compute such gradient field from the elevation values given at the TIN vertices, and how to simplify it effectively in order to reduce the number of critical elements. We demonstrate the effectiveness and scalability of our approach over large terrains by developing algorithms for extracting the cells of the Morse complexes as well as the graph joining the critical elements from the discrete gradient field. We compare implementations of our approach on a widely-used and compact adjacency-based topological data structure for a TIN and on a compact spatio-topological data structure that we have recently developed, the PR-star quadtree.

References

  1. T. Banchoff. Critical points and curvature for embedded polyhedral surfaces. American Mathematical Monthly, 77(5):475--485, 1970.Google ScholarGoogle ScholarCross RefCross Ref
  2. S. Biasotti, L. De Floriani, B. Falcidieno, P. Frosini, D. Giorgi, C. Landi, L. Papaleo, and M. Spagnuolo. Describing shapes by geometrical-topological properties of real functions. ACM Computing Surveys, 40(4):12:1--12:87, October 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J.-D. Boissonnat, O. Devillers, and S. Hornus. Incremental construction of the Delaunay triangulation and the Delaunay graph in medium dimension. In Proceedings Symposium on Computational Geometry, SCG '09, pages 208--216, New York, NY, USA, 2009. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. Canino, L. De Floriani, and K. Weiss. IA*: An adjacency-based representation for non-manifold simplicial shapes in arbitrary dimensions. Computers & Graphics, 35(3):747--753, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. P. Cignoni, C. Montani, C. Rocchini, and R. Scopigno. External memory management and simplification of huge meshes. IEEE Transactions on Visualization and Computer Graphics, 9(4):525--537, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. L. Čomić and L. De Floriani. Dimension-independent simplification and refinement of Morse complexes. Graphical Models, 73(5):261--285, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. L. Čomić, L. De Floriani, and F. Iuricich. Modeling three-dimensional morse and morse-smale complexes. In M. Breuç, A. Bruckstein, and P. Maragos, editors, Innovations for Shape Analysis, Mathematics and Visualization, pages 3--34. Springer Berlin Heidelberg, 2013.Google ScholarGoogle Scholar
  8. L. De Floriani, R. Fellegara, F. Iuricich, and K. Weiss. A spatial approach to morphological feature extraction from irregularly sampled scalar fields. In Proceedings ACM SIGSPATIAL International Workshop on GeoStreaming, IWGS '12, pages 40--47, November 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. L. De Floriani, R. Fellegara, and P. Magillo. Spatial indexing on tetrahedral meshes. In Proceedings ACM SIGSPATIAL GIS, GIS '10, pages 506--509. ACM, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. T. Dey, J. Levine, and A. Slatton. Localized Delaunay refinement for sampling and meshing. Computer Graphics Forum, 29(5):1723--1732, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  11. H. Edelsbrunner. Algorithms in Combinatorial Geometry. Springer Verlag, Berlin, 1987. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. H. Edelsbrunner, J. Harer, V. Natarajan, and V. Pascucci. Morse-Smale complexes for piecewise linear 3-manifolds. In Proceedings Symposium on Computational Geometry, pages 361--370. ACM, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. H. Edelsbrunner, J. Harer, and A. Zomorodian. Hierarchical Morse complexes for piecewise linear 2-manifolds. In Proceedings Symposium on Computational Geometry, pages 70--79. ACM, 2001. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. L. D. Floriani and A. Hui. Data structures for simplicial complexes: An analysis and a comparison. In M. Desbrun and H. Pottmann, editors, Symposium on Geometry Processing, volume 255 of ACM International Conference Proceeding Series, pages 119--128. Eurographics Association, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. L. D. Floriani, A. Hui, D. Panozzo, and D. Canino. A dimension-independent data structure for simplicial complexes. In S. M. Shontz, editor, IMR, pages 403--420. Springer, 2010.Google ScholarGoogle Scholar
  16. R. Forman. Morse theory for cell complexes. Advances in Mathematics, 134: 90--145, 1998.Google ScholarGoogle ScholarCross RefCross Ref
  17. D. Günther, J. Reininghaus, H. Wagner, and I. Hotz. Efficient computation of 3D Morse-Smale complexes and persistent homology using discrete Morse theory. The Visual Computer, 28(10):959--969, 2012.Google ScholarGoogle ScholarCross RefCross Ref
  18. A. Gyulassy, P. T. Bremer, B. Hamann, and V. Pascucci. A practical approach to Morse-Smale complex computation: Scalability and generality. IEEE Transactions on Visualization and Computer Graphics, 14(6):1619--1626, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. A. Gyulassy, P.-T. Bremer, and V. Pascucci. Computing Morse-Smale complexes with accurate geometry. IEEE Transactions on Visualization and Computer Graphics, 18(12):2014--2022, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. A. Gyulassy, N. Kotava, M. Kim, C. Hansen, H. Hagen, and V. Pascucci. Direct feature visualization using Morse-Smale complexes. IEEE Transactions on Visualization and Computer Graphics, 18(9):1549--1562, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. F. Iuricich. Multi-resolution shape analysis based on discrete morse decompositions. In PhD thesis, University of Genova - DIBRIS, Italy, 2014.Google ScholarGoogle Scholar
  22. Y. Matsumoto. An introduction to Morse theory. Translations of mathematical monographs, volume 208. American Mathematical Society, 2002.Google ScholarGoogle Scholar
  23. J. Milnor. Morse Theory. Princeton University Press, New Jersey, 1963.Google ScholarGoogle Scholar
  24. G. M. Nielson. Tools for triangulations and tetrahedralizations and constructing functions defined over them. In G. Nielson, H. Hagen, and H. Müller, editors, Scientific Visualization: Overviews, Methodologies and Techniques, chapter 20, pages 429--525. IEEE Computer Society, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. A. Paoluzzi, F. Bernardini, C. Cattani, and V. Ferrucci. Dimension-independent modeling with simplicial complexes. ACM Transactions on Graphics, 12(1):56--102, January 1993. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. V. Robins, P. Wood, and A. Sheppard. Theory and algorithms for constructing discrete Morse complexes from grayscale digital images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8):1646--1658, August 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. H. Samet. Foundations of Multidimensional and Metric Data Structures. The Morgan Kaufmann series in computer graphics and geometric modeling. Morgan Kaufmann, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. N. Shivashankar and V. Natarajan. Parallel computation of 3D Morse-Smale complexes. Computer Graphics Forum, 31(3):965--974, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. K. Weiss, R. Fellegara, L. De Floriani, and M. Velloso. The PR-star octree: A spatio-topological data structure for tetrahedral meshes. In Proceedings ACM SIGSPATIAL GIS, GIS '11, pages 92--101. ACM, November 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. K. Weiss, F. Iuricich, R. Fellegara, and L. De Floriani. A primal/dual representation for discrete morse complexes on tetrahedral meshes. In Computer Graphics Forum, volume 32, pages 361--370. Wiley Online Library, 2013. Google ScholarGoogle ScholarDigital LibraryDigital Library

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            cover image ACM Conferences
            SIGSPATIAL '14: Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
            November 2014
            651 pages
            ISBN:9781450331319
            DOI:10.1145/2666310

            Copyright © 2014 ACM

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            Publication History

            • Published: 4 November 2014

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            SIGSPATIAL '14 Paper Acceptance Rate39of184submissions,21%Overall Acceptance Rate220of1,116submissions,20%

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