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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

Qiqi Hou , Randall Rauwendaal , Zifeng Li , Hoang Le , Farzad Farhadzadeh , Fatih Porikli , Alexei Bourd , Amir Said This is my paper
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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Cited by 5 Pith papers

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

  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. Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication

    cs.DC 2026-06 unverdicted novelty 6.0 of 10

    Splaxel achieves up to 7.6x speedup in distributed 3DGS training on scenes with up to 120M Gaussians by using pixel-level communication and visibility prediction while preserving reconstruction quality.

  3. Depth Peeling for High-Fidelity Gaussian-Enhanced Surfel Rendering

    cs.GR 2026-05 unverdicted novelty 6.0 of 10

    DP-GES augments Gaussian-enhanced surfels with semi-transparent boundaries and uses depth peeling for per-pixel ordering to enable sort-free rendering with correct transmittance and reduced artifacts.

  4. Softmax-GS: Generalized Gaussians Learning When to Blend or Bound

    cs.CV 2026-04 unverdicted novelty 6.0 of 10

    Softmax-GS generalizes 3D Gaussians with learnable softmax competition in overlaps to enable a spectrum from blending to crisp edges while preserving order invariance and transmittance.

  5. SparseOIT: Improving Order-Independent Transparency 3DGS via Active Set Method

    cs.GR 2026-04 unverdicted novelty 5.0 of 10

    SparseOIT uses active set optimization on sparse dependencies from OIT-modified 3DGS rendering equations to improve reconstruction speed and quality for transparent materials.

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