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SG-Splatting: Accelerating 3D Gaussian Splatting with Spherical Gaussians

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arxiv 2501.00342 v1 pith:3VMXQWSX submitted 2024-12-31 cs.CV

classification cs.CV
keywords sphericalcolorgaussiansrenderingrepresentationharmonicsnovelquality
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GSBF: Gaussian Splatting for Environment-Aware Beamforming

    cs.AI 2026-08 conditional novelty 6.0 of 10

    GSBF renders a 3D Gaussian scene into an angular propagation map and converts it into constant-modulus beamforming vectors, achieving higher spectral efficiency than codebook beam sweeping in simulation.

  2. CTRL-GS: Cascaded Temporal Residue Learning for 4D Gaussian Splatting

    cs.CV 2025-05 conditional novelty 5.0 of 10

    CTRL-GS represents dynamic Gaussian scenes as cascaded video-segment-frame residuals, improving reconstruction quality over 4D-GS on several dynamic-view benchmarks.

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