Brent Zoomers, Maarten Wijnants, Ivan Molenaers, Joni Vanherck, Jeroen Put, Lode Jorissen, Nick Michiels
{"title":"PRoGS: Progressive Rendering of Gaussian Splats","authors":"Brent Zoomers, Maarten Wijnants, Ivan Molenaers, Joni Vanherck, Jeroen Put, Lode Jorissen, Nick Michiels","doi":"arxiv-2409.01761","DOIUrl":null,"url":null,"abstract":"Over the past year, 3D Gaussian Splatting (3DGS) has received significant\nattention for its ability to represent 3D scenes in a perceptually accurate\nmanner. However, it can require a substantial amount of storage since each\nsplat's individual data must be stored. While compression techniques offer a\npotential solution by reducing the memory footprint, they still necessitate\nretrieving the entire scene before any part of it can be rendered. In this\nwork, we introduce a novel approach for progressively rendering such scenes,\naiming to display visible content that closely approximates the final scene as\nearly as possible without loading the entire scene into memory. This approach\nbenefits both on-device rendering applications limited by memory constraints\nand streaming applications where minimal bandwidth usage is preferred. To\nachieve this, we approximate the contribution of each Gaussian to the final\nscene and construct an order of prioritization on their inclusion in the\nrendering process. Additionally, we demonstrate that our approach can be\ncombined with existing compression methods to progressively render (and stream)\n3DGS scenes, optimizing bandwidth usage by focusing on the most important\nsplats within a scene. Overall, our work establishes a foundation for making\nremotely hosted 3DGS content more quickly accessible to end-users in\nover-the-top consumption scenarios, with our results showing significant\nimprovements in quality across all metrics compared to existing methods.","PeriodicalId":501480,"journal":{"name":"arXiv - CS - Multimedia","volume":"11 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Multimedia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.01761","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Over 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.