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2017 | OriginalPaper | Chapter

Beyond Multi-view Stereo: Shading-Reflectance Decomposition

Authors : Jean Mélou, Yvain Quéau, Jean-Denis Durou, Fabien Castan, Daniel Cremers

Published in: Scale Space and Variational Methods in Computer Vision

Publisher: Springer International Publishing

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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 \(l^1\)-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.

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Footnotes
1
This model is valid for greyscale images. To handle RGB images, our approach can be applied independently to each color channel.
 
2
In order to compare comparable things, we scale the estimated albedos in each part, so that its median is equal to the associated ground truth value.
 
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Metadata
Title
Beyond Multi-view Stereo: Shading-Reflectance Decomposition
Authors
Jean Mélou
Yvain Quéau
Jean-Denis Durou
Fabien Castan
Daniel Cremers
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-58771-4_55

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