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Communication Dans Un Congrès Année : 2017

Beyond Multi-view Stereo: Shading-Reflectance Decomposition

Résumé

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.
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Dates et versions

hal-01787432 , version 1 (07-05-2018)

Identifiants

Citer

Jean Mélou, Yvain Quéau, Jean-Denis Durou, Fabien Castan, Daniel Cremers. Beyond Multi-view Stereo: Shading-Reflectance Decomposition. 6th International Conference on Scale Space and Variational Methods in Computer Vision (SSVM 2017), Jun 2017, Kolding, Denmark. pp. 694-705, ⟨10.1007/978-3-319-58771-4_55⟩. ⟨hal-01787432⟩
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