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Reducing the Memory Footprint of 3D Gaussian Splatting

Published: 13 May 2024 Publication History

Abstract

3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and realtime rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze the reasons for this, identifying three main areas where storage can be reduced: the number of 3D Gaussian primitives used to represent a scene, the number of coefficients for the spherical harmonics used to represent directional radiance, and the precision required to store Gaussian primitive attributes. We present a solution to each of these issues. First, we propose an efficient, resolution-aware primitive pruning approach, reducing the primitive count by half. Second, we introduce an adaptive adjustment method to choose the number of coefficients used to represent directional radiance for each Gaussian primitive, and finally a codebook-based quantization method, together with a half-float representation for further memory reduction. Taken together, these three components result in a x27 reduction in overall size on disk on the standard datasets we tested, along with a x1.7 speedup in rendering speed. We demonstrate our method on standard datasets and show how our solution results in significantly reduced download times when using the method on a mobile device (see Fig. 1).

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Cited By

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  • (2025)Fast 3D Gaussian Splatting Rendering via Easily Integrable ImprovementsIEEE Signal Processing Letters10.1109/LSP.2024.352137932(381-385)Online publication date: 2025
  • (2024)Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric VideosACM Transactions on Graphics10.1145/368792643:6(1-15)Online publication date: 19-Dec-2024
  • (2024)GS‐Octree: Octree‐based 3D Gaussian Splatting for Robust Object‐level 3D Reconstruction Under Strong LightingComputer Graphics Forum10.1111/cgf.1520643:7Online publication date: 24-Oct-2024
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      cover image Proceedings of the ACM on Computer Graphics and Interactive Techniques
      Proceedings of the ACM on Computer Graphics and Interactive Techniques  Volume 7, Issue 1
      May 2024
      399 pages
      EISSN:2577-6193
      DOI:10.1145/3665094
      Issue’s Table of Contents
      Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      Publication History

      Published: 13 May 2024
      Published in PACMCGIT Volume 7, Issue 1

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      Author Tags

      1. 3D gaussian splatting
      2. memory reduction
      3. novel view synthesis
      4. radiance fields

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      View all
      • (2025)Fast 3D Gaussian Splatting Rendering via Easily Integrable ImprovementsIEEE Signal Processing Letters10.1109/LSP.2024.352137932(381-385)Online publication date: 2025
      • (2024)Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric VideosACM Transactions on Graphics10.1145/368792643:6(1-15)Online publication date: 19-Dec-2024
      • (2024)GS‐Octree: Octree‐based 3D Gaussian Splatting for Robust Object‐level 3D Reconstruction Under Strong LightingComputer Graphics Forum10.1111/cgf.1520643:7Online publication date: 24-Oct-2024
      • (2024)Factorized Multi-Resolution HashGrid for Efficient Neural Radiance Fields: Execution on Edge-DevicesIEEE Robotics and Automation Letters10.1109/LRA.2024.34604199:11(10272-10279)Online publication date: Nov-2024
      • (2024)A review of recent advances in 3D Gaussian Splatting for optimization and reconstructionImage and Vision Computing10.1016/j.imavis.2024.105304151(105304)Online publication date: Nov-2024
      • (2024)MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute TransformationComputer Vision – ECCV 202410.1007/978-3-031-73414-4_25(434-452)Online publication date: 25-Oct-2024

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