dc.contributor.author | Inoue, Naoto | en_US |
dc.contributor.author | Ito, Daichi | en_US |
dc.contributor.author | Hold-Geoffroy, Yannick | en_US |
dc.contributor.author | Mai, Long | en_US |
dc.contributor.author | Price, Brian | en_US |
dc.contributor.author | Yamasaki, Toshihiko | en_US |
dc.contributor.editor | Panozzo, Daniele and Assarsson, Ulf | en_US |
dc.date.accessioned | 2020-05-24T12:53:12Z | |
dc.date.available | 2020-05-24T12:53:12Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 1467-8659 | |
dc.identifier.uri | https://doi.org/10.1111/cgf.13943 | |
dc.identifier.uri | https://diglib.eg.org:443/handle/10.1111/cgf13943 | |
dc.description.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 geometryaware 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. | en_US |
dc.publisher | The Eurographics Association and John Wiley & Sons Ltd. | en_US |
dc.rights | Attribution 4.0 International License | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | |
dc.subject | Computing methodologies | |
dc.subject | Image | |
dc.subject | based rendering | |
dc.title | RGB2AO: Ambient Occlusion Generation from RGB Images | en_US |
dc.description.seriesinformation | Computer Graphics Forum | |
dc.description.sectionheaders | Images and Videos | |
dc.description.volume | 39 | |
dc.description.number | 2 | |
dc.identifier.doi | 10.1111/cgf.13943 | |
dc.identifier.pages | 451-462 | |