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Sort-free Gaussian Splatting via Weighted Sum Rendering

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arxiv 2410.18931 v2 pith:JNEHDD4X submitted 2024-10-24 cs.CV

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

Recently, 3D Gaussian Splatting (3DGS) has emerged as a significant advancement in 3D scene reconstruction, attracting considerable attention due to its ability to recover high-fidelity details while maintaining low complexity. Despite the promising results achieved by 3DGS, its rendering performance is constrained by its dependence on costly non-commutative alpha-blending operations. These operations mandate complex view dependent sorting operations that introduce computational overhead, especially on the resource-constrained platforms such as mobile phones. In this paper, we propose Weighted Sum Rendering, which approximates alpha blending with weighted sums, thereby removing the need for sorting. This simplifies implementation, delivers superior performance, and eliminates the "popping" artifacts caused by sorting. Experimental results show that optimizing a generalized Gaussian splatting formulation to the new differentiable rendering yields competitive image quality. The method was implemented and tested in a mobile device GPU, achieving on average $1.23\times$ faster rendering.

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Forward citations

Cited by 2 Pith papers

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

  1. CaT-GS: Efficient 3DGS Rendering for Large Scale Scenes via Inter-frame Caching and Tile Scheduling

    cs.CV 2026-07 conditional novelty 7.0 of 10

    CaT-GS speeds up 3D Gaussian Splatting rendering by caching inter-frame preprocessing and splitting heavy tile-rasterization loads across GPU work units.

  2. Efficient 3D Gaussian Splatting with Axis-Shared Rasterization and Order-independent Transmittance

    cs.GR 2025-06 conditional novelty 5.0 of 10

    A 3D Gaussian Splatting accelerator uses axis-shared rasterization and a trained MLP to replace sorting, reporting large speedups over edge GPUs with about 1 dB PSNR loss.

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