Gaussian Light Transport

2026-09-10

AI Summary

该研究提出了一种基于13D高斯混合模型的新方法来解决全局光照计算问题，通过优化参数直接估计并结合高效剔除策略实现快速实时渲染。

Abstract

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.

Problem

• Gaussian Mixture Model
• Light Transport Equation
• Global Illumination
• Real Time Rendering
• Memory Efficiency

Innovation

• Gaussian Mixture Model
• Light Transport Equation
• Real-time Rendering
• Efficient Culling Strategy