RGB2AO: Ambient Occlusion Generation from RGB Images

We present RGB2AO, a novel task to generate ambient occlusion (AO) from a single RGB image instead of screen space buffers such as depth and normal. RGB2AO produces a new image filter that creates a non‐directional shading effect that darkens enclosed and sheltered areas. RGB2AO aims to enhance two...

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Vydané v:	Computer graphics forum Ročník 39; číslo 2; s. 451 - 462
Hlavní autori:	Inoue, N., Ito, D., Hold‐Geoffroy, Y., Mai, L., Price, B., Yamasaki, T.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Oxford Blackwell Publishing Ltd 01.05.2020
Predmet:	Artificial neural networks CCS Concepts Color imagery Computing methodologies → Image‐based rendering Image contrast Image enhancement Image filters Occlusion Shading
ISSN:	0167-7055, 1467-8659
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Abstract	We present RGB2AO, a novel task to generate ambient occlusion (AO) from a single RGB image instead of screen space buffers such as depth and normal. RGB2AO produces a new image filter that creates a non‐directional shading effect that darkens enclosed and sheltered areas. RGB2AO aims to enhance two 2D image editing applications: image composition and geometry‐aware contrast enhancement. We first collect a synthetic dataset consisting of pairs of RGB images and AO maps. Subsequently, we propose a model for RGB2AO by supervised learning of a convolutional neural network (CNN), considering 3D geometry of the input image. Experimental results quantitatively and qualitatively demonstrate the effectiveness of our model.
AbstractList	We present RGB2AO, a novel task to generate ambient occlusion (AO) from a single RGB image instead of screen space buffers such as depth and normal. RGB2AO produces a new image filter that creates a non‐directional shading effect that darkens enclosed and sheltered areas. RGB2AO aims to enhance two 2D image editing applications: image composition and geometry‐aware contrast enhancement. We first collect a synthetic dataset consisting of pairs of RGB images and AO maps. Subsequently, we propose a model for RGB2AO by supervised learning of a convolutional neural network (CNN), considering 3D geometry of the input image. Experimental results quantitatively and qualitatively demonstrate the effectiveness of our model.
Author	Mai, L. Price, B. Ito, D. Yamasaki, T. Inoue, N. Hold‐Geoffroy, Y.
Author_xml	– sequence: 1 givenname: N. surname: Inoue fullname: Inoue, N. email: inoue@hal.t.u-tokyo.ac.jp organization: The University of Tokyo – sequence: 2 givenname: D. surname: Ito fullname: Ito, D. email: dito@adobe.com organization: Adobe Research – sequence: 3 givenname: Y. surname: Hold‐Geoffroy fullname: Hold‐Geoffroy, Y. email: holdgeof@adobe.com organization: Adobe Research – sequence: 4 givenname: L. surname: Mai fullname: Mai, L. email: mai.t.long88@gmail.com organization: Adobe Research – sequence: 5 givenname: B. surname: Price fullname: Price, B. email: bprice@adobe.com organization: Adobe Research – sequence: 6 givenname: T. surname: Yamasaki fullname: Yamasaki, T. email: yamasaki@hal.t.u-tokyo.ac.jp organization: The University of Tokyo
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Snippet	We present RGB2AO, a novel task to generate ambient occlusion (AO) from a single RGB image instead of screen space buffers such as depth and normal. RGB2AO...
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SubjectTerms	Artificial neural networks CCS Concepts Color imagery Computing methodologies → Image‐based rendering Image contrast Image enhancement Image filters Occlusion Shading
Title	RGB2AO: Ambient Occlusion Generation from RGB Images
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