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StochasticSplats: Stochastic Rasterization for Sorting-Free 3D Gaussian Splatting

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arxiv 2503.24366 v1 pith:6LDXZMPP submitted 2025-03-31 cs.CV cs.GR

Shakiba Kheradmand , Delio Vicini , George Kopanas , Dmitry Lagun , Kwang Moo Yi , Mark Matthews , Andrea Tagliasacchi This is my paper
classification cs.CVcs.GR
keywords renderinggaussianmethodrasterizationsplattingcarlofastermonte
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3D Gaussian splatting (3DGS) is a popular radiance field method, with many application-specific extensions. Most variants rely on the same core algorithm: depth-sorting of Gaussian splats then rasterizing in primitive order. This ensures correct alpha compositing, but can cause rendering artifacts due to built-in approximations. Moreover, for a fixed representation, sorted rendering offers little control over render cost and visual fidelity. For example, and counter-intuitively, rendering a lower-resolution image is not necessarily faster. In this work, we address the above limitations by combining 3D Gaussian splatting with stochastic rasterization. Concretely, we leverage an unbiased Monte Carlo estimator of the volume rendering equation. This removes the need for sorting, and allows for accurate 3D blending of overlapping Gaussians. The number of Monte Carlo samples further imbues 3DGS with a way to trade off computation time and quality. We implement our method using OpenGL shaders, enabling efficient rendering on modern GPU hardware. At a reasonable visual quality, our method renders more than four times faster than sorted rasterization.

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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. TemporalGS: Training-Free Plug-and-Play Acceleration for 3D Gaussian Splatting Rendering via Temporal Priors

    cs.CV 2026-07 conditional novelty 6.5 of 10

    TemporalGS accelerates 3DGS rendering up to 1.48× without training by culling redundant Gaussians and selectively rendering only tiles that cannot be warped from temporal geometry and appearance buffers.

  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. RLGS: Reinforcement Learning-Based Adaptive Hyperparameter Tuning for Gaussian Splatting

    cs.GR 2025-08 conditional novelty 5.0 of 10

    RLGS uses online policy-gradient agents to adaptively scale 3DGS hyperparameters during training, yielding small but consistent rendering quality gains.

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