3DGS.zip: A survey on 3D Gaussian Splatting Compression Methods

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dc.contributor.authorBagdasarian, Milena T.en_US
dc.contributor.authorKnoll, Paulen_US
dc.contributor.authorLi, Yi-Hsinen_US
dc.contributor.authorBarthel, Florianen_US
dc.contributor.authorHilsmann, Annaen_US
dc.contributor.authorEisert, Peteren_US
dc.contributor.authorMorgenstern, Wielanden_US
dc.contributor.editorMemari, Pooranen_US
dc.contributor.editorGryaditskaya, Yuliaen_US
dc.date.accessioned2025-05-09T09:40:06Z
dc.date.available2025-05-09T09:40:06Z
dc.date.issued2025
dc.description.abstract3D Gaussian Splatting (3DGS) has emerged as a cutting-edge technique for real-time radiance field rendering, offering state-ofthe- art performance in terms of both quality and speed. 3DGS models a scene as a collection of three-dimensional Gaussians, with additional attributes optimized to conform to the scene's geometric and visual properties. Despite its advantages in rendering speed and image fidelity, 3DGS is limited by its significant storage and memory demands. These high demands make 3DGS impractical for mobile devices or headsets, reducing its applicability in important areas of computer graphics. To address these challenges and advance the practicality of 3DGS, this state-of-the-art report (STAR) provides a comprehensive and detailed examination of two complementary yet fundamentally distinct strategies: compression and compaction. Compression techniques focus on reducing the file size by encoding Gaussian attributes more efficiently. In contrast, compaction methods directly optimize the scene's structure by optimizing the number of Gaussian primitives. Notably, while methods in both categories aim to maintain or improve quality, each while minimizing its respective attributes-file size for compression and the number of Gaussians for compaction-compaction does not necessarily lead to smaller file sizes; it specifically targets improved efficiency during rendering, making it distinct from compression. We introduce the basic mathematical concepts underlying the analyzed methods, as well as key implementation details and design choices. Our report thoroughly discusses similarities and differences among the methods, as well as their respective advantages and disadvantages. We establish a consistent framework for comparing the surveyed methods based on key performance metrics and datasets. Specifically, since these methods have been developed in parallel and over a short period of time, currently, no comprehensive comparison exists. This survey, for the first time, presents a unified framework to evaluate 3DGS compression techniques. To facilitate the continuous monitoring of emerging methodologies, we maintain a dedicated website that will be regularly updated with new techniques and revisions of existing findings. Overall, this STAR provides an intuitive starting point for researchers interested in exploring the rapidly growing field of 3DGS compression. By comprehensively categorizing and evaluating existing compression and compaction strategies, our work advances the understanding and practical application of 3DGS in computationally constrained environments.en_US
dc.description.documenttypestar
dc.description.number2
dc.description.sectionheadersState of the Art Reports
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume44
dc.identifier.doi10.1111/cgf.70078
dc.identifier.issn1467-8659
dc.identifier.pages26 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70078
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70078
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Information systems → Data compression; Computing methodologies → Rasterization; General and reference → Surveys and overviews
dc.subjectInformation systems → Data compression
dc.subjectComputing methodologies → Rasterization
dc.subjectGeneral and reference → Surveys and overviews
dc.title3DGS.zip: A survey on 3D Gaussian Splatting Compression Methodsen_US
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