Skip to Main content Skip to Navigation
Journal articles

A unifying and rigorous Shape From Shading method adapted to realistic data and applications

Emmanuel Prados 1 Fabio Camilli 2 Olivier Faugeras 3
1 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
3 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique - ENS Paris, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS-PSL - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : We propose a new method for the Lambertian Shape From Shading (SFS) problem based on the notion of Crandall-Lions viscosity solution. This method has the advantage of requiring the knowledge of the solution (the surface to be reconstructed) only on some part of the boundary and/or of the singular set (the set of the points at maximal intensity). Moreover it unifies in an unique mathematical formulation the works of Rouy and Tourin, Falcone et al., Prados and Faugeras, based on the notion of viscosity solutions and the work of Dupuis and Oliensis dealing with classical solutions and value functions. Also, it allows to generalize their results to the "perspective SFS" problem.
Document type :
Journal articles
Complete list of metadata

Cited literature [59 references]  Display  Hide  Download


https://hal.inria.fr/inria-00377391
Contributor : Emmanuel Prados Connect in order to contact the contributor
Submitted on : Tuesday, April 21, 2009 - 4:28:25 PM
Last modification on : Thursday, March 17, 2022 - 10:08:31 AM
Long-term archiving on: : Thursday, June 10, 2010 - 9:20:53 PM

Files

prados-etal-JMIV-2006.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Emmanuel Prados, Fabio Camilli, Olivier Faugeras. A unifying and rigorous Shape From Shading method adapted to realistic data and applications. Journal of Mathematical Imaging and Vision, Springer Verlag, 2006, 25 (3), pp.307--328. ⟨10.1007/s10851-006-6899-x⟩. ⟨inria-00377391⟩

Share

Metrics

Record views

317

Files downloads

190