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DC poleHodnotaJazyk
dc.contributor.authorSoares, Gonçalo
dc.contributor.authorPereira, João Madeiras
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-09-01T08:56:48Z
dc.date.available2021-09-01T08:56:48Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 325-334.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45039
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectvzdělávací rámec pro sledování paprskůcs
dc.subjectNvidia RTXcs
dc.subjectVulkancs
dc.subjecttrasování cestycs
dc.subjectAI-accelerated Denoisercs
dc.titleLift: An Educational Interactive Stochastic Ray Tracing Framework with AI-Accelerated Denoiseren
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedReal-time physically based rendering has long been looked at as the holy grail in Computer Graphics. With theintroduction of Nvidia RTX-enabled GPUs family, light transport simulations under real-time constraint startedto look like a reality. This paper presents Lift, an educational framework written in C++ that explores the RTXhardware pipeline by using the low-level Vulkan API and its Ray Tracing extension, recently made available byKhronos Group. Furthermore, to accomplish low variance rendered images, we integrated the AI-based denoiseravailable from the Nvidia ́s OptiX framework. Lift’s development arose primarily in the context of the graduate3D Programming course taught at Instituto Superior Técnico and Master Theses focused on Real-Time Ray Trac-ing and provides the foundations for laboratory assignments and projects development. The platform aims to makeeasier students to learn and to develop, by programming the shaders of the RT pipeline, their physically-based ren-dering approaches and to compare them with the built-in progressive unidirectional and bidirectional path tracers.The GUI allows a user to specify camera settings and navigation speed, to select the input scene as well as therendering method, to define the number of samples per pixel and the path length as well as to denoise the generatedimage either every frame or just the final frame. Statistics related with the timings, image resolution and totalnumber of accumulated samples are provided too. Such platform will teach that nowadays physically-accurateimages can be rendered in real-time under different lighting conditions and how well a denoiser can reconstructimages rendered with just one sample per pixel.en
dc.subject.translatedEducational Ray Tracing frameworken
dc.subject.translatedNvidia RTXen
dc.subject.translatedVulkanen
dc.subject.translatedpath tracingen
dc.subject.translatedAI-accelerated Denoiseren
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.36
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

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