Manifold Reconstruction in Arbitrary Dimensions Using Witness Complexes

It is a well-established fact that the witness complex is closelyrelated to the restricted Delaunay triangulation in lowdimensions. Specifically, it has been proved that the witness complexcoincides with the restricted Delaunay triangulation on curves, and isstill a subset of it on surfaces, under mild samplingassumptions. Unfortunately, these results do not extend tohigher-dimensional manifolds, even under stronger samplingconditions. In this paper, we show how the sets of witnesses andlandmarks can be enriched, so that the nice relations that existbetween both complexes still hold on higher-dimensional manifolds. Wealso use our structural results to devise an algorithm thatreconstructs manifolds of any arbitrary dimension or co-dimension atdifferent scales. The algorithm combines a farthest-point refinementscheme with a vertex pumping strategy. It is very simple conceptually,and it does not require the input point sample W to be sparse. Itstime complexity is bounded by c(d) |W|2, where c(d) is a constantdepending solely on the dimension d of the ambient space.

[1]  Frédéric Chazal,et al.  A Sampling Theory for Compact Sets in Euclidean Space , 2006, SCG '06.

[2]  Vijayan K. Asari,et al.  A New Nonlinear Dimensionality Reduction Technique for Pose and Lighting Invariant Face Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[3]  Steve Oudot,et al.  Towards persistence-based reconstruction in euclidean spaces , 2007, SCG '08.

[4]  Olivier Devillers,et al.  Complexity of Delaunay triangulation for points on lower-dimensional polyhedra , 2007, SODA '07.

[5]  Sunghee Choi,et al.  A Simple Algorithm for Homeomorphic Surface Reconstruction , 2002, Int. J. Comput. Geom. Appl..

[6]  Herbert Edelsbrunner,et al.  Topological Persistence and Simplification , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[7]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[8]  Michel Verleysen,et al.  Nonlinear Dimensionality Reduction , 2021, Computer Vision.

[9]  J. Boissonnat,et al.  Provably good sampling and meshing of Lipschitz surfaces , 2006, SCG '06.

[10]  Joachim Giesen,et al.  Shape Dimension and Intrinsic Metric from Samples of Manifolds , 2004, Discret. Comput. Geom..

[11]  Joachim Giesen,et al.  Shape dimension and intrinsic metric from samples of manifolds with high co-dimension , 2003, SCG '03.

[12]  Mikhail Belkin,et al.  Semi-Supervised Learning on Riemannian Manifolds , 2004, Machine Learning.

[13]  Leonidas J. Guibas,et al.  Geodesic Delaunay triangulation and witness complex in the plane , 2008, SODA '08.

[14]  Jean-Daniel Boissonnat,et al.  Complexity of the delaunay triangulation of points on surfaces the smooth case , 2003, SCG '03.

[15]  Herbert Edelsbrunner,et al.  Geometry and Topology for Mesh Generation , 2001, Cambridge monographs on applied and computational mathematics.

[16]  Afra Zomorodian,et al.  Computing Persistent Homology , 2004, SCG '04.

[17]  Leonidas J. Guibas,et al.  Reconstruction Using Witness Complexes , 2007, SODA '07.

[18]  Xiang-Yang Li Generating well-shaped d-dimensional Delaunay Meshes , 2003, Theor. Comput. Sci..

[19]  V. De Silva,et al.  A Weak Definition of Delaunay Triangulation , 2003 .

[20]  Kim Steenstrup Pedersen,et al.  The Nonlinear Statistics of High-Contrast Patches in Natural Images , 2003, International Journal of Computer Vision.

[21]  Marshall W. Bern,et al.  Surface Reconstruction by Voronoi Filtering , 1998, SCG '98.

[22]  Frédéric Chazal,et al.  Weak feature size and persistent homology: computing homology of solids in Rn from noisy data samples , 2005, SCG.

[23]  David Cohen-Steiner,et al.  Stability of Persistence Diagrams , 2005, Discret. Comput. Geom..

[24]  Xiang-Yang Li Generating Well-Shaped d-dimensional Delaunay Meshes , 2001, COCOON.

[25]  Sunghee Choi,et al.  A simple algorithm for homeomorphic surface reconstruction , 2000, SCG '00.

[26]  Joachim Giesen,et al.  Delaunay Triangulation Based Surface Reconstruction , 2006 .

[27]  Frédéric Chazal,et al.  Topology guaranteeing manifold reconstruction using distance function to noisy data , 2006, SCG '06.

[28]  Herbert Edelsbrunner,et al.  Sliver exudation , 2000, J. ACM.

[29]  Vin de Silva,et al.  A weak characterisation of the Delaunay triangulation , 2008 .

[30]  Steve Oudot On the Topology of the Restricted Delaunay Triangulation and Witness Complex in Higher Dimensions , 2008, ArXiv.

[31]  Tamal K. Dey,et al.  Manifold reconstruction from point samples , 2005, SODA '05.

[32]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[33]  Herbert Edelsbrunner,et al.  Algorithms in Combinatorial Geometry , 1987, EATCS Monographs in Theoretical Computer Science.

[34]  P. McMullen The maximum numbers of faces of a convex polytope , 1970 .

[35]  Stephen Smale,et al.  Finding the Homology of Submanifolds with High Confidence from Random Samples , 2008, Discret. Comput. Geom..

[36]  Gunnar E. Carlsson,et al.  Topological estimation using witness complexes , 2004, PBG.

[37]  David Eppstein,et al.  The Crust and the beta-Skeleton: Combinatorial Curve Reconstruction , 1998, Graph. Model. Image Process..

[38]  Dimitrios Gunopulos,et al.  Non-linear dimensionality reduction techniques for classification and visualization , 2002, KDD.

[39]  Herbert Edelsbrunner,et al.  Weak witnesses for Delaunay triangulations of submanifolds , 2007, Symposium on Solid and Physical Modeling.

[40]  Herbert Edelsbrunner,et al.  Sliver exudation , 1999, SCG '99.

[41]  Herbert Edelsbrunner,et al.  Alpha-Beta Witness Complexes , 2007, WADS.

[42]  Tamal K. Dey,et al.  A simple provable algorithm for curve reconstruction , 1999, SODA '99.

[43]  Jean-Daniel Boissonnat,et al.  Effective computational geometry for curves and surfaces , 2006 .

[44]  Tamal K. Dey,et al.  Shape Dimension and Approximation from Samples , 2002, SODA '02.