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arXiv · 2609.25604

Ultra-fast Neural Inference for Stochastic Gaussian Splatting Denoising

Chenxiao Hu·Hao Zhang·Yanchen Zhang·Meng Gai·Guoping Wang·Sheng Li
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Abstract

Stochastic rendering eliminates the sorting and alpha blending process in Gaussian splatting, at the cost of introducing spatial noise. Formulating temporal denoising over the pixel stream shared by view-consistent stochastic splatting renderers, we propose a temporal neural denoiser validated on stochastic 2D Gaussian Splatting rendering, combining dual-path exponential moving average accumulation, per-pixel learned trust prediction for history validation, a fixed anisotropic spatial filter and a variance-gated composition with stabilization. The denoiser suppresses the noise, achieving temporally stable, visually compelling outputs during free camera navigation, all while retaining the sort-free, blend-free rasterization performance. The combined pipeline retains a PSNR gap to sorted alpha-blending renderers, but the denoiser's overhead stays below the time saved by removing sorting and blending.

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Chenxiao Hu, Hao Zhang, Yanchen Zhang, Meng Gai, Guoping Wang, Sheng Li. 2026-09-22. Ultra-fast Neural Inference for Stochastic Gaussian Splatting Denoising. https://arxiv.org/abs/2609.25604

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