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.

翻译：三维高斯泼溅（3DGS）是一种流行的辐射场方法，已衍生出许多面向特定应用的扩展。大多数变体依赖于相同的核心算法：对高斯泼溅进行深度排序，然后按图元顺序进行光栅化。这确保了正确的阿尔法合成，但由于内置的近似处理，可能导致渲染伪影。此外，对于固定表示形式，排序渲染在渲染成本与视觉保真度的调控方面能力有限。例如，反直觉的是，渲染较低分辨率的图像并不必然更快。本研究通过将三维高斯泼溅与随机光栅化相结合，解决了上述局限性。具体而言，我们采用体积渲染方程的无偏蒙特卡洛估计器。这消除了排序需求，并实现了重叠高斯函数的精确三维混合。蒙特卡洛采样数量进一步为3DGS提供了权衡计算时间与质量的调控手段。我们使用OpenGL着色器实现了该方法，能够在现代GPU硬件上实现高效渲染。在合理的视觉质量下，本方法的渲染速度比排序光栅化快四倍以上。