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Parameter-free and Multigrid Convergent Digital Curvature Estimators

Jérémy Levallois 1 David Coeurjolly 1, * Jacques-Olivier Lachaud 2
* Corresponding author
1 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : In many geometry processing applications, the estimation of differential geometric quantities such as curvature or normal vector field is an essential step. Focusing on multigrid convergent estimators, most of them require a user specified parameter to define the scale at which the analysis is performed (size of a convolution kernel, size of local patches for polynomial fitting, etc). In a previous work, we have proposed a new class of estimators on digital shape boundaries based on Integral Invariants. In this paper, we propose new variants of these estimators which are parameter-free and ensure multigrid convergence in 2D. As far as we know, these are the first parameter-free multigrid convergent curvature estimators.
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Jérémy Levallois, David Coeurjolly, Jacques-Olivier Lachaud. Parameter-free and Multigrid Convergent Digital Curvature Estimators. 18th International Conference on Discrete Geometry for Computer Imagery (DGCI 2014), Sep 2014, Siena, Italy. pp.162-175, ⟨10.1007/978-3-319-09955-2_14⟩. ⟨hal-01118476⟩



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