A probabilistic approach to reducing the algebraic complexity of computing Delaunay triangulations

Computing Delaunay triangulations in R d involves evaluating the so-called in_sphere predicate that determines if a point x lies inside, on or outside the sphere circumscribing d + 1 points p0;:::;pd. This predicate reduces to evaluating the sign of a multivariate polynomial of degree d + 2 in the coordinates of the points x;p0;:::;pd. Despite much progress on exact geometric computing, the fact that the degree of the polynomial increases with d makes the evaluation of the sign of such a polynomial problematic except in very low dimensions. In this paper, we propose a new approach that is based on the witness complex, a weak form of the Delaunay complex introduced by Carlsson and de Silva. The witness complex Wit(L;W ) is defined from two sets L and W in some metric space X: a finite set of points L on which the complex is built, and a set W of witnesses that serves as an approximation of X. A fundamental result of de Silva states that Wit(L;W ) = Del(L) if W = X = R d . In this paper, we give conditions on L that ensure that the witness complex and the Delaunay triangulation coincide when W is a finite set, and we introduce a new perturbation scheme to compute a perturbed set L 0 close to L such that Del(L 0 ) = Wit(L 0 ;W ). Our perturbation algorithm is a geometric application of the Moser-Tardos constructive proof of the Lovasz local lemma. The only numerical operations we use are (squared) distance comparisons (i.e., predicates of degree 2). The time-complexity of the algorithm is sublinear in jWj. Interestingly, although the algorithm does not compute any measure of simplex quality, a lower bound on the thickness of the output simplices can be guaranteed.

[1]  Sariel Har-Peled Geometric Approximation Algorithms , 2011 .

[2]  David L. Millman,et al.  Computing planar Voronoi diagrams in double precision: a further example of degree-driven algorithm design , 2010, SoCG '10.

[3]  Jean-Daniel Boissonnat,et al.  The Simplex Tree: An Efficient Data Structure for General Simplicial Complexes , 2012, Algorithmica.

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

[5]  Noga Alon,et al.  The Probabilistic Method , 2015, Fundamentals of Ramsey Theory.

[6]  Steve Oudot,et al.  Only distances are required to reconstruct submanifolds , 2014, Comput. Geom..

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

[8]  Jean-Daniel Boissonnat,et al.  The stability of Delaunay Triangulations , 2013, Int. J. Comput. Geom. Appl..

[9]  Markus Gross,et al.  Point-Based Graphics , 2007 .

[10]  Kurt Mehlhorn,et al.  Algorithms for Complex Shapes with Certified Numerics and Topology Controlled Perturbation for Delaunay Triangulations , 2022 .

[11]  Geometriae Dedicata,et al.  Geometriae Dedicata , 2003 .

[12]  Jean-Daniel Boissonnat,et al.  Delaunay stability via perturbations , 2014, Int. J. Comput. Geom. Appl..

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

[14]  Leonidas J. Guibas,et al.  Epsilon geometry: building robust algorithms from imprecise computations , 1989, SCG '89.

[15]  Gábor Tardos,et al.  A constructive proof of the general lovász local lemma , 2009, JACM.

[16]  Dan Halperin,et al.  Controlled Perturbation for Certified Geometric Computing with Fixed-Precision Arithmetic , 2010, ICMS.