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arXiv · 2610.05576

SteadySplats: Resampling of Low-Variance Gaussians for High-Fidelity Stochastic Rendering

Felix Windisch·Thomas Köhler·Lukas Radl·Chris Wyman·Georgios Kopanas·Bernhard Kerb·Markus Steinberger
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Abstract

Stochastic order-independent transparency enables efficient and elegant rendering of primitive-based radiance fields like 3D Gaussian Splatting models, but remains impractical due to the inherent visible noise in the output. We propose a principled approach to minimize high-frequency noise, addressing its sources at the representation and image synthesis level. During stochastic rendering, our history-based spatial resampling scheme drastically accelerates image convergence, while temporal importance resampling ensures coherence under camera movement. During training, a color regularizer implicitly reduces the variance along view rays in the 3DGS models. With these properties, our optimized, Vulkan-based renderer effectively mitigates output noise at low and high sample counts, achieving a substantial 13~dB PSNR increase in quality over previous stochastic methods at 1 sample per pixel and quickly converging to sorted 3DGS with an average L1 error of less than $10^{-4}$.

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Felix Windisch, Thomas Köhler, Lukas Radl, Chris Wyman, Georgios Kopanas, Bernhard Kerb, Markus Steinberger. 2026-10-04. SteadySplats: Resampling of Low-Variance Gaussians for High-Fidelity Stochastic Rendering. https://arxiv.org/abs/2610.05576

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