Pré-Publication, Document De Travail Année : 2016

Variational Methods for Normal Integration

Résumé

The need for an efficient method of integration of a dense normal field is inspired by several computer vision tasks, such as shape-from-shading, pho-tometric stereo, deflectometry, etc. Inspired by edge-preserving methods from image processing, we study in this paper several variational approaches for normal integration , with a focus on non-rectangular domains, free boundary and depth discontinuities. We first introduce a new discretization for quadratic integration, which is designed to ensure both fast recovery and the ability to handle non-rectangular domains with a free boundary. Yet, with this solver, discontinuous surfaces can be handled only if the scene is first segmented into pieces without discontinuity. Hence, we then discuss several discontinuity-preserving strategies. Those inspired, respectively , by the Mumford-Shah segmentation method and by anisotropic diffusion, are shown to be the most effective for recovering discontinuities.

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Est une version de hal-02118476 Article Yvain Quéau, Jean-Denis Durou, Jean-François Aujol. Variational Methods for Normal Integration. Journal of Mathematical Imaging and Vision, 2018, 60 (4), pp.609-632. ⟨10.1007/s10851-017-0777-6⟩. ⟨hal-02118476⟩

Dates et versions

hal-01334351 , version 1 (20-06-2016)
hal-01334351 , version 2 (02-03-2017)
hal-01334351 , version 3 (18-09-2017)

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Yvain Quéau, Jean-Denis Durou, Jean-François Aujol. Variational Methods for Normal Integration. 2016. ⟨hal-01334351v3⟩
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