SG-Splatting: Accelerating 3D Gaussian Splatting with Spherical Gaussians
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Abstract (arXiv)
3D Gaussian Splatting is emerging as a state-of-the-art technique in novel view synthesis, recognized for its impressive balance between visual quality, speed, and rendering efficiency. However, reliance on third-degree spherical harmonics for color representation introduces significant storage demands and computational overhead, resulting in a large memory footprint and slower rendering speed. We introduce SG-Splatting with Spherical Gaussians based color representation, a novel approach to enhance rendering speed and quality in novel view synthesis. Our method first represents view-dependent color using Spherical Gaussians, instead of three degree spherical harmonics, which largely reduces the number of parameters used for color representation, and significantly accelerates the rendering process. We then develop an efficient strategy for organizing multiple Spherical Gaussians, optimizing their arrangement to achieve a balanced and accurate scene representation. To further improve rendering quality, we propose a mixed representation that combines Spherical Gaussians with low-degree spherical harmonics, capturing both high- and low-frequency color information effectively. SG-Splatting also has plug-and-play capability, allowing it to be easily integrated into existing systems. This approach improves computational efficiency and overall visual fidelity, making it a practical solution for real-time applications.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Dec 2024.
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Citing papers per year: 2025: 4; 2026: 4.
8 papers.
- 2025MEGS2: Memory-Efficient Gaussian Splatting via Spherical Gaussians and Unified Pruningcited by 5
- 2025GSta: Efficient Training Scheme with Siestaed Gaussians for Monocular 3D Scene Reconstructioncited by 1
- 2025Yesnt: Are Diffusion Relighting Models Ready for Capture Stage Compositing? A Hybrid Alternative to Bridge the Gapcited by 1
- 2026GSBF: Gaussian Splatting for Environment-Aware Beamformingcited by 1
- 2025CTRL-GS: Cascaded Temporal Residue Learning for 4D Gaussian Splattingcited by 0
- 2026Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAMcited by 0
- 2026Bake It Till You Make It: Ultrafast Spatial Texture-Atlas Splattingcited by 0
- 2026NRF-GS: Neural Residual Fields for Expressive and Compact Gaussian Splattingcited by 0
- 2026NRF-GS: Neural Residual Fields for Expressive and Compact Gaussian Splatting29 Sep 2026
- 2026GSBF: Gaussian Splatting for Environment-Aware Beamforming6 Aug 2026
- 2026Bake It Till You Make It: Ultrafast Spatial Texture-Atlas Splatting15 Jul 2026
- 2026Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM23 Jun 2026
- 2025Yesnt: Are Diffusion Relighting Models Ready for Capture Stage Compositing? A Hybrid Alternative to Bridge the Gap27 Oct 2025
- 2025MEGS2: Memory-Efficient Gaussian Splatting via Spherical Gaussians and Unified Pruning7 Sep 2025
- 2025CTRL-GS: Cascaded Temporal Residue Learning for 4D Gaussian Splatting23 May 2025
- 2025GSta: Efficient Training Scheme with Siestaed Gaussians for Monocular 3D Scene Reconstruction9 Apr 2025