发表机构
Graz University of Technology; NVIDIA; Google DeepMind; University of Copenhagen（格拉茨技术大学; 英伟达; 谷歌DeepMind; 哥本哈根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对3D高斯溅射随机渲染中的可见噪声问题，提出历史空间重采样、时间重要性重采样及颜色正则化方法，在低采样数下显著提升PSNR并快速收敛。
AI 中文摘要
随机顺序无关透明度能够实现基于基元的辐射场（如3D高斯溅射模型）的高效且优雅的渲染，但由于输出中固有的可见噪声，该方法仍不实用。我们提出了一种原则性方法来最小化高频噪声，从表示和图像合成层面解决其来源。在随机渲染过程中，我们基于历史的空间重采样方案大幅加速图像收敛，而时间重要性重采样确保相机移动下的一致性。在训练过程中，颜色正则化器隐式降低3DGS模型沿视图射线的方差。凭借这些特性，我们优化的基于Vulkan的渲染器在低和高采样数下有效减轻输出噪声，在每像素1个样本时，相较于先前随机方法实现质量上显著的13 dB PSNR提升，并迅速收敛到排序的3DGS，平均L1误差小于$10^{-4}$。
英文摘要
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 $10^{-4}$.
