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Theoretically Based Robust Algorithms for Tracking Intersection Curves of Two Deforming Parametric Surfaces

  • Xianming Chen
  • Richard F. Riesenfeld
  • Elaine Cohen
  • James Damon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4077)

Abstract

This paper applies singularity theory of mappings of surfaces to 3-space and the generic transitions occurring in their deformations to develop algorithms for continuously and robustly tracking the intersection curves of two deforming parametric spline surfaces, when the deformation is represented as a family of generalized offset surfaces. This paper presents the mathematical framework, and develops algorithms accordingly, to continuously and robustly track the intersection curves of two deforming parametric surfaces, with the deformation represented as generalized offset vector fields. The set of intersection curves of 2 deforming surfaces over all time is formulated as an implicit 2-manifold \(\mathcal{I}\) in the augmented (by time domain) parametric space \(\mathbb R^5\). Hyper-planes corresponding to some fixed time instants may touch \(\mathcal{I}\) at some isolated transition points, which delineate transition events, i.e., the topological changes to the intersection curves. These transition points are the 0-dimensional solution to a rational system of 5 constraints in 5 variables, and can be computed efficiently and robustly with a rational constraint solver using subdivision and hyper-tangent bounding cones. The actual transition events are computed by contouring the local osculating paraboloids. Away from any transition points, the intersection curves do not change topology and evolve according to a simple evolution vector field that is constructed in the euclidean space in which the surfaces are embedded.

Keywords

Transition Point Tangent Plane Geometric Design Intersection Curve Surface Intersection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xianming Chen
    • 1
  • Richard F. Riesenfeld
    • 1
  • Elaine Cohen
    • 1
  • James Damon
    • 2
  1. 1.School of ComputingUniversity of UtahSalt Lake CityUSA
  2. 2.Department of MathematicsUniversity of North CarolinaChapel HillUSA

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