用随机采样加速3D高斯点云重建与渲染，无需排序且支持真实光影。
Stochastic Ray Tracing for the Reconstruction of 3D Gaussian Splatting
- 每条光线仅采样少量高斯点，避免排序开销，实现无序高效渲染。
- 标准场景重建质量与光栅化方法相当，速度远超传统光线追踪。
- 支持真实阴影反射，适合需要高保真光照的场景重建任务。
基于光线追踪的3D高斯点云（3DGS）方法克服了光栅化方法在针孔相机假设、阴影不准确及缺乏天然反射折射等方面的局限，但因需对每条光线上的所有相交高斯点进行排序而效率较低。现有光线追踪方法仍依赖光栅化风格的近似技术（如阴影贴图），削弱了光线追踪应有的通用性。本文提出首个可微、免排序的随机光线追踪框架，首次实现对标准与可重光照3DGS场景的重建与渲染。核心是像素颜色梯度的无偏蒙特卡洛估计器，每条光线仅评估少量采样的高斯点，跳过排序步骤。在标准3DGS中，本方法在重建质量和速度上与光栅化方法相当，显著优于基于排序的光线追踪；在可重光照3DGS中，同一估计器驱动逐高斯着色，并使用完全光线追踪的阴影射线，重建保真度明显优于先前工作。
原文摘要 · Abstract (English)
Ray-tracing-based 3D Gaussian splatting (3DGS) methods overcome the limitations of rasterization -- rigid pinhole camera assumptions, inaccurate shadows, and lack of native reflection or refraction -- but remain slower due to the cost of sorting all intersecting Gaussians along every ray. Moreover, existing ray-tracing methods still rely on rasterization-style approximations such as shadow mapping for relightable scenes, undermining the generality that ray tracing promises. We present a differentiable, sorting-free stochastic formulation for ray-traced 3DGS -- the first framework that uses stochastic ray tracing to both reconstruct and render standard and relightable 3DGS scenes. At its core is an unbiased Monte Carlo estimator for pixel-color gradients that evaluates only a small sampled subset of Gaussians per ray, bypassing the need for sorting. For standard 3DGS, our method matches the reconstruction quality and speed of rasterization-based 3DGS while substantially outperforming sorting-based ray tracing. For relightable 3DGS, the same stochastic estimator drives per-Gaussian shading with fully ray-traced shadow rays, delivering notably higher reconstruction fidelity than prior work.
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