Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/45808
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dc.contributor.advisorMichiels, Nick-
dc.contributor.advisorJorissen, Lode-
dc.contributor.authorZOOMERS, Brent-
dc.contributor.authorWIJNANTS, Maarten-
dc.contributor.authorMolenaers, Ivan-
dc.contributor.authorVANHERCK, Joni-
dc.contributor.authorPUT, Jeroen-
dc.contributor.authorJORISSEN, Lode-
dc.contributor.authorMICHIELS, Nick-
dc.date.accessioned2025-04-03T13:37:10Z-
dc.date.available2025-04-03T13:37:10Z-
dc.date.issued2024-
dc.date.submitted2025-03-24T13:34:09Z-
dc.identifier.citation-
dc.identifier.urihttp://hdl.handle.net/1942/45808-
dc.description.abstractOver the past year, 3D Gaussian Splatting (3DGS) has received significant attention for its ability to represent 3D scenes in a perceptually accurate manner. However, it can require a substantial amount of storage since each splat's individual data must be stored. While compression techniques offer a potential solution by reducing the memory footprint, they still necessitate retrieving the entire scene before any part of it can be rendered. In this work, we introduce a novel approach for progressively rendering such scenes, aiming to display visible content that closely approximates the final scene as early as possible without loading the entire scene into memory. This approach benefits both on-device rendering applications limited by memory constraints and streaming applications where minimal bandwidth usage is preferred. To achieve this, we approximate the contribution of each Gaussian to the final scene and construct an order of prioritization on their inclusion in the rendering process. Additionally, we demonstrate that our approach can be combined with existing compression methods to progressively render (and stream) 3DGS scenes, optimizing bandwidth usage by focusing on the most important splats within a scene. Overall, our work establishes a foundation for making remotely hosted 3DGS content more quickly accessible to end-users in over-the-top consumption scenarios, with our results showing significant improvements in quality across all metrics compared to existing methods.-
dc.description.sponsorshipThis research was partly funded by the specialized FWO fellowship grant (1SHDZ24N), the European Union (HORIZONMAX-R,MixedAugmentedandExtended Reality Media Pipeline, 101070072), the Flanders 20 Make’s XRTwin SBO project (R-12528) and the Special Research Fund (BOF) of Hasselt University (R-14360). This work was made possible with support from MAXVR-INFRA, a scalable and flexible infrastructure that facilitates the transition to digital-physical work environments.-
dc.language.isoen-
dc.rightsCC BY-NC-ND 4.0-
dc.subjectComputer Science - Computer Vision and Pattern Recognition-
dc.subjectComputer Science - Computer Vision and Pattern Recognition-
dc.subjectComputer Science - Multimedia-
dc.subject.otherComputer Science - Computer Vision and Pattern Recognition-
dc.titlePRoGS: Progressive Rendering of Gaussian Splats-
dc.typePreprint-
local.bibliographicCitation.conferencedate2025, Feb 28-Mar 4-
local.bibliographicCitation.conferencenameIEEE/CVF Winter Conference on Applications of Computer Vision (WACV)-
local.bibliographicCitation.conferenceplaceTucson(Arizona), USA-
local.bibliographicCitation.jcatO-
local.type.refereedNon-Refereed-
local.type.specifiedPreprint-
dc.identifier.arxiv2409.01761-
dc.identifier.urlhttp://arxiv.org/abs/2409.01761v1-
dc.description.otherFWO: R-14292 BOF: R-14436-
local.provider.typeArXiv-
local.uhasselt.internationalno-
item.contributorZOOMERS, Brent-
item.contributorWIJNANTS, Maarten-
item.contributorMolenaers, Ivan-
item.contributorVANHERCK, Joni-
item.contributorPUT, Jeroen-
item.contributorJORISSEN, Lode-
item.contributorMICHIELS, Nick-
item.fullcitationZOOMERS, Brent; WIJNANTS, Maarten; Molenaers, Ivan; VANHERCK, Joni; PUT, Jeroen; JORISSEN, Lode & MICHIELS, Nick (2024) PRoGS: Progressive Rendering of Gaussian Splats.-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
Appears in Collections:Research publications
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