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Variational methods for normal integration

Quéau, Yvain and Durou, Jean-Denis and Aujol, Jean-François Variational methods for normal integration. (2018) Journal of Mathematical Imaging and Vision, 60 (4). 609-632. ISSN 0924-9907

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Official URL: https://doi.org/10.1007/s10851-017-0777-6


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, photometric stereo, deflectometry. 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.

Item Type:Article
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Institut universitaire de France - IUF (FRANCE)
Other partners > Technische Universität München - TUM (GERMANY)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Université de Bordeaux (FRANCE)
Laboratory name:
Deposited On:22 Apr 2020 16:09

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