Multigrid convergent principal curvature estimators in digital geometry - MGMI
Article Dans Une Revue Computer Vision and Image Understanding Année : 2014

Multigrid convergent principal curvature estimators in digital geometry

Résumé

In many geometry processing applications, the estimation of differential geometric quantities such as curvature or normal vector field is an essential step. In this paper, we investigate a new class of estimators on digital shape boundaries based on integral invariants (Pottmann et al., 2007) [39]. More precisely, we provide both proofs of multigrid convergence of principal curvature estimators and a complete experimental evaluation of their performances.
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Dates et versions

hal-01119434 , version 1 (20-05-2024)

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David Coeurjolly, Jacques-Olivier Lachaud, Jérémy Levallois. Multigrid convergent principal curvature estimators in digital geometry. Computer Vision and Image Understanding, 2014, 129 (1), pp.27-41. ⟨10.1016/j.cviu.2014.04.013⟩. ⟨hal-01119434⟩
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