Full metadata record
DC poleHodnotaJazyk
dc.contributor.authorBurkus, Viktória
dc.contributor.authorKárpáti, Attila
dc.contributor.authorSzécsi, László
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-09-01T06:18:49Z
dc.date.available2021-09-01T06:18:49Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 237-244.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45029
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectpočítačová grafikacs
dc.subjectmetaballscs
dc.subjectgenerativní neurální síťcs
dc.subjectrekonstrukce povrchucs
dc.titleParticle-Based Fluid Surface Rendering with Neural Networksen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedSurface reconstruction for particle-based fluid simulation is a computational challenge on par with the simula-tion itself. In real-time applications, splatting-style rendering approaches based on forward rendering of particleimpostors are prevalent, but they suffer from noticeable artifacts.In this paper, we present a technique that combines forward rendering simulated features with deep-learning imagemanipulation to improve the rendering quality of splatting-style approaches to be perceptually similar to ray tracingsolutions, circumventing the cost, complexity, and limitations of exact fluid surface rendering by replacing it withthe flat cost of a neural network pass. Our solution is based on the idea of training generative deep neural networkswith image pairs consisting of cheap particle impostor renders and ground truth high quality ray-traced images.en
dc.subject.translatedcomputer graphicsen
dc.subject.translatedmetaballsen
dc.subject.translatedgenerative neural networken
dc.subject.translatedsurface reconstructionen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.26
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

Soubory připojené k záznamu:
Soubor Popis VelikostFormát 
J29.pdfPlný text6,22 MBAdobe PDFZobrazit/otevřít
Zobrazit minimální záznam Zobrazit statistiky


Použijte tento identifikátor k citaci nebo jako odkaz na tento záznam: http://hdl.handle.net/11025/45029

Všechny záznamy v DSpace jsou chráněny autorskými právy, všechna práva vyhrazena.

hledání
navigace
  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Sborníky ZČU / UWB Proceedings
  4. WSCG: Full Papers
  5. WSCG 2021: Full Papers Proceedings