Normals and Curvature Estimation for Digital Surfaces Based on Convolutions

Sébastien Fourey (1) , Rémy Malgouyres (2)

1 Equipe Image - Laboratoire GREYC - UMR6072
2 LLAIC1 - Laboratoire de Logique, Algorithmique et Informatique

In this paper, we present a method that we call on-surface convolution which extends the classical notion of a 2D digital filter to the case of digital surfaces (following the cuberille model). We also define an averaging mask with local support which, when applied with the iterated convolution operator, behaves like an averaging with large support. The interesting property of the latter averaging is the way the resulting weights are distributed: they tend to decrease following a “continuous” geodesic distance within the surface. We eventually use the iterated averaging followed by convolutions with differentiation masks to estimate partial derivatives and then normal vectors over a surface. We provide an heuristics based on [14] for an optimal mask size and show results.

Mots clés: normal estimation; convolution; digital surfaces

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]; Traitement des images [eess.IV]

Sébastien Fourey, Rémy Malgouyres. Normals and Curvature Estimation for Digital Surfaces Based on Convolutions. 14th IAPR International conference, DGCI 2008, Apr 2008, Lyon, France. pp.287-298, ⟨10.1007/978-3-540-79126-3_26⟩. ⟨hal-00333772⟩