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arXiv · 1709.05965

Variational Methods for Normal Integration

Yvain Quéau·Jean-Denis Durou·Jean-François Aujol
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Abstract

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, 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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Yvain Quéau, Jean-Denis Durou, Jean-François Aujol. 2017-09-18. Variational Methods for Normal Integration. https://arxiv.org/abs/1709.05965

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