Beyond multi-view stereo: Shading-reflectance decomposition

Melou J, Queau Y, Durou JD, Castan F, Cremers D (2017)

Publication Type: Conference contribution

Publication year: 2017

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Verlag

Book Volume: 10302 LNCS

Pages Range: 694-705

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Kolding, DNK

ISBN: 9783319587707

DOI: 10.1007/978-3-319-58771-4_55

Abstract

We introduce a variational framework for separating shading and reflectance from a series of images acquired under different angles, when the geometry has already been estimated by multi-view stereo. Our formulation uses an l1-TV variational framework, where a robust photometric-based data term enforces adequation to the images, total variation ensures piecewise-smoothness of the reflectance, and an additional multi-view consistency term is introduced for resolving the arising ambiguities. Optimisation is carried out using an alternating optimisation strategy building upon iteratively reweighted least-squares. Preliminary results on both a synthetic dataset, using various lighting and reflectance scenarios, and a real dataset, confirm the potential of the proposed approach.

Involved external institutions

Université Fédérale de Toulouse Midi-Pyrénées FR France (FR) Technische Universität München (TUM) DE Germany (DE) Mikros Image FR France (FR)

How to cite

APA:

Melou, J., Queau, Y., Durou, J.-D., Castan, F., & Cremers, D. (2017). Beyond multi-view stereo: Shading-reflectance decomposition. In Francois Lauze, Yiqiu Dong, Anders Bjorholm Dahl (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 694-705). Kolding, DNK: Springer Verlag.

MLA:

Melou, Jean, et al. "Beyond multi-view stereo: Shading-reflectance decomposition." Proceedings of the 6th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2017, Kolding, DNK Ed. Francois Lauze, Yiqiu Dong, Anders Bjorholm Dahl, Springer Verlag, 2017. 694-705.

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