@inproceedings{1b4717d1220a48468cea2a80618a44bb,
title = "Beyond multi-view stereo: Shading-reflectance decomposition",
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.",
keywords = "Multi-view, Reflectance, Shading, Variational methods",
author = "Jean M{\'e}lou and Yvain Qu{\'e}au and Durou, \{Jean Denis\} and Fabien Castan and Daniel Cremers",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 6th International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2017 ; Conference date: 04-06-2017 Through 08-06-2017",
year = "2017",
doi = "10.1007/978-3-319-58771-4\_55",
language = "English",
isbn = "9783319587707",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "694--705",
editor = "Francois Lauze and Yiqiu Dong and Dahl, \{Anders Bjorholm\}",
booktitle = "Scale Space and Variational Methods in Computer Vision - 6th International Conference, SSVM 2017, Proceedings",
}