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dc.contributor.authorMiller, Markus
dc.contributor.authorNischwitz, Alfred
dc.contributor.authorWestermann, Rüdiger
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-08-31T05:45:53Z-
dc.date.available2021-08-31T05:45:53Z-
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 31-40.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45007
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectsvětlocs
dc.subjectzdrojcs
dc.subjectsměrcs
dc.subjectodhadcs
dc.subjectrekonstrukcecs
dc.subjectRGBcs
dc.subjecthluboké učenícs
dc.titleDeep Light Direction Reconstruction from single RGB imagesen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedIn augmented reality applications, consistent illumination between virtual and real objects is important for creatingan immersive user experience. Consistent illumination can be achieved by appropriate parameterisation of thevirtual illumination model, that is consistent with real-world lighting conditions. In this study, we developed amethod to reconstruct the general light direction from red-green-blue (RGB) images of real-world scenes using amodified VGG-16 neural network. We reconstructed the general light direction as azimuth and elevation angles. Toavoid inaccurate results caused by coordinate uncertainty occurring at steep elevation angles, we further introducedstereographically projected coordinates. Unlike recent deep-learning-based approaches for reconstructing the lightsource direction, our approach does not require depth information and thus does not rely on special red-green-blue-depth (RGB-D) images as input.en
dc.subject.translatedlighten
dc.subject.translatedsourceen
dc.subject.translateddirectionen
dc.subject.translatedestimationen
dc.subject.translatedreconstructionen
dc.subject.translatedRGBen
dc.subject.translateddeep learningen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.4
dc.type.statusPeer-revieweden
Appears in Collections:WSCG 2021: Full Papers Proceedings

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