Variational Reflectance Estimation from Multi-view Images

Jean Mélou, Yvain Quéau, Jean Denis Durou, Fabien Castan, Daniel Cremers

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

We tackle the problem of reflectance estimation from a set of multi-view images, assuming known geometry. The approach we put forward turns the input images into reflectance maps, through a robust variational method. The variational model comprises an image-driven fidelity term and a term which enforces consistency of the reflectance estimates with respect to each view. If illumination is fixed across the views, then reflectance estimation remains under-constrained: A regularization term, which ensures piecewise-smoothness of the reflectance, is thus used. Reflectance is parameterized in the image domain, rather than on the surface, which makes the numerical solution much easier, by resorting to an alternating majorization–minimization approach. Experiments on both synthetic and real-world datasets are carried out to validate the proposed strategy.

Original languageEnglish
Pages (from-to)1527-1546
Number of pages20
JournalJournal of Mathematical Imaging and Vision
Volume60
Issue number9
DOIs
StatePublished - 1 Nov 2018

Keywords

  • Multi-view
  • Reflectance
  • Shading
  • Variational methods

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