高斯光传输
Gaussian Light Transport
- INRIA - Grenoble University（法国国家信息与自动化研究所-格勒诺布尔大学）
- University of Edinburgh（爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出用13维高斯混合模型表达光传输方程解，通过最小化渲染方程残差直接估计参数，并引入剔除策略实现毫秒级实时全局光照渲染，内存需求远低于神经渲染方法。
中文摘要 AI 辅助
我们提出了一种计算全局光照的新方法，将光传输方程的解表示为关于位置、方向、表面法线和材质属性的13维高斯混合模型。我们表明，在高斯表示中包含场景属性可大幅减少函数数量并加速评估。与基于诺伊曼级数的传统光传输方法不同，我们模型的参数通过最小化渲染方程的残差直接估计。虽然优化和渲染都需要重复评估高维高斯函数的线性组合，但我们引入了一种高效的剔除策略，以保持优化的可行性并实时生成渲染结果。我们的表示能够快速渲染光传输方程的与视图无关的解，实现毫秒级的渲染时间，且内存需求仅为传统神经渲染方法的一小部分。
英文摘要
We present a novel method for computing global illumination by expressing the solution to the light transport equation as a 13D Gaussian mixture model over positions, directions, surface normals, and material properties. We show that including scene properties in the Gaussian representation drastically reduces the number of functions and speeds up evaluation. As opposed to traditional light transport methods based on Neumann series, the parameters of our model are directly estimated by minimizing the residual of the rendering equation. While both optimization and rendering require repeated evaluations of a linear combination of high-dimensional Gaussian functions, we introduce an efficient culling strategy to keep the optimization tractable and produce renderings in real time. Our representation enables to render fast, view-independent solutions to the light transport equation, achieving rendering times on the order of milliseconds, with a fraction of the memory requirements of conventional neural rendering approaches.
