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2019 | OriginalPaper | Buchkapitel

Intrinsic Decomposition by Learning from Varying Lighting Conditions

verfasst von : Gregoire Nieto, Mohammad Rouhani, Philippe Robert

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

Intrinsic image decomposition describes an image based on its reflectance and shading components. In this paper we tackle the problem of estimating the diffuse reflectance from a sequence of images captured from a fixed viewpoint under various illuminations. To this end we propose a deep learning approach to avoid heuristics and strong assumptions on the reflectance prior. We compare two network architectures: one classic ‘U’ shaped Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) composed of Convolutional Gated Recurrent Units (CGRU). We train our networks on a new dataset specifically designed for the task of intrinsic decomposition from sequences. We test our networks on MIT and BigTime datasets and outperform state-of-the-art algorithms both qualitatively and quantitatively.

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Titel: Intrinsic Decomposition by Learning from Varying Lighting Conditions
verfasst von: Gregoire Nieto
Mohammad Rouhani
Philippe Robert
Verlag: Springer International Publishing
Buch: Advances in Visual Computing
Print ISBN: 978-3-030-33719-3

Electronic ISBN: 978-3-030-33720-9

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-33720-9_50

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