用随机渲染取代深度排序，让3D高斯点云更快更准地渲染。
StochasticSplats: Stochastic Rasterization for Sorting-Free 3D Gaussian Splatting
- 引入无偏蒙特卡洛估计，无需深度排序直接渲染。
- 相同质量下比传统方法快4倍以上，且支持灵活控制计算量。
- 适合追求高效实时渲染的3D重建与可视化应用。
3D高斯点云（3DGS）是一种流行的辐射场方法，其大多数变体依赖相同的算法：对高斯点进行深度排序后按顺序光栅化，以确保正确的透明合成。然而，该过程因固有近似导致渲染伪影，且对固定表示而言，排序渲染难以灵活控制渲染成本和视觉质量——例如，低分辨率图像未必更快。本文通过将3DGS与随机光栅化结合，解决上述问题。具体地，我们采用体积渲染方程的无偏蒙特卡洛估计器，消除排序需求，并实现重叠高斯点的精确三维混合。蒙特卡洛采样数量可调节计算时间与质量之间的权衡。我们使用OpenGL着色器实现该方法，可在现代GPU上高效运行。在合理视觉质量下，相比传统排序光栅化，本方法速度提升超过4倍。
原文摘要 · Abstract (English)
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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