Curvature and torsion estimators based on parametric curve fitting

  • Authors:
  • Thomas Lewiner;João D. Gomes, Jr.;Hélio Lopes;Marcos Craizer

  • Affiliations:
  • PUC-Rio, Departamento de Matemática, Matmídia Project, Rio de Janeiro, Brazil and INRIA, Géométrica Project, Sophia Antipolis, France;PUC-Rio, Departamento de Matemática, Matmídia Project, Rio de Janeiro, Brazil;PUC-Rio, Departamento de Matemática, Matmídia Project, Rio de Janeiro, Brazil;PUC-Rio, Departamento de Matemática, Matmídia Project, Rio de Janeiro, Brazil

  • Venue:
  • Computers and Graphics
  • Year:
  • 2005

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Abstract

Many applications of geometry processing and computer vision rely on geometric properties of curves, particularly, their curvature. Several methods have already been proposed to estimate the curvature of a planar curve, most of them for curves in digital spaces. This work proposes a new scheme for estimating curvature and torsion of planar and spatial curves, based on weighted least-squares fitting and local arc-length approximation. The method is simple enough to admit a convergence analysis that takes into account the effect of noise in the samples. The implementation of the method is compared to other curvature estimation methods showing a good performance. Applications to prediction in geometry compression are presented both as a practical application and as a validation of this new scheme.