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DC poleHodnotaJazyk
dc.contributor.authorGojoković, Katarina
dc.contributor.authorLesar, Žiga
dc.contributor.authorMarolt, Matija
dc.contributor.editorSkala, Václav
dc.date.accessioned2024-07-21T08:56:33Z-
dc.date.available2024-07-21T08:56:33Z-
dc.date.issued2024-
dc.identifier.citationJournal of WSCG. 2024, vol. 32, no. 1-2, p. 81-70.en
dc.identifier.issn1213 – 6972
dc.identifier.issn1213 – 6980 (CD-ROM)
dc.identifier.issn1213 – 6964 (on-line)
dc.identifier.urihttp://hdl.handle.net/11025/57345
dc.format10 s.cs_CZ
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs_CZ
dc.rights© Václav Skala - UNION Agencyen
dc.subjectodrazové sondycs
dc.subjectinterpolacecs
dc.subjectkonvoluční neuronové sítěcs
dc.titleReflection probe interpolation for fast and accurate rendering of reflective materialsen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersion-
dc.description.abstract-translatedIn this paper, we aim to improve rendering reflections using environment maps on moving reflective objects. Such scenarios require multiple reflection probes to be positioned at various locations in a scene. During rendering, the closest reflection probe is typically chosen as the environment map of a specific object, resulting in sharp transitions between the rendered reflections when the object moves around the scene. To solve this problem, we developed two convolutional neural networks that dynamically synthesize the best possible environment map at a given point in the scene. The first network generates an environment map from the coordinates of a given point through a decoder architecture. In the second approach, we triangulated the scene and captured environment maps at the triangle vertices – these represent reflection probes. The second network receives at the input three environment maps captured at the vertices of the triangle containing the query point, along with the distances between the query point and the vertices. Through an encoder-decoder architecture, the second network performs smart interpolation of the three environment maps. Both approaches are based on the phenomenon of overfitting, which made it necessary to train each network individually for specific scenes. Both networks are successful at predicting environment maps at arbitrary locations in the scene, even if these locations were not part of the training set. The accuracy of the predictions strongly depends on the complexity of the scene itself.en
dc.subject.translatedreflection probesen
dc.subject.translatedinterpolationen
dc.subject.translatedconvolutional neural networksen
dc.identifier.doihttps://www.doi.org/10.24132/JWSCG.2024.7
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 32, number 1-2 (2024)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 32 (2024)
  6. Volume 32, number 1-2 (2024)