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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Veröffentlicht in:Computer graphics forum Jg. 39; H. 2; S. 451 - 462
Hauptverfasser: Inoue, N., Ito, D., Hold‐Geoffroy, Y., Mai, L., Price, B., Yamasaki, T.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.05.2020
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13943