Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
525569 | Computer Vision and Image Understanding | 2014 | 15 Pages |
Abstract
•We propose discrete principal curvature estimators based on integral invariants.•We prove the multigrid convergence of these estimators.•We provide an experimental evaluation on synthetic and real data.
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
David Coeurjolly, Jacques-Olivier Lachaud, Jérémy Levallois,