We describe a method to use Spherical Gaussians with free directions and arbitrary sharpness and amplitude to approximate the precomputed local light field for any point on a surface in a scene. This allows for a high‐quality reconstruction of these light fields in a manner that can be used to render the surfaces with precomputed global illumination in real‐time with very low cost both in memory and performance. We also extend this concept to represent the illumination‐weighted environment visibility, allowing for high‐quality reflections of the distant environment with both surface‐material properties and visibility taken into account. We treat obtaining the Spherical Gaussians as an optimization problem for which we train a Convolutional Neural Network to produce appropriate values for each of the Spherical Gaussians' parameters. We define this CNN in such a way that the produced parameters can be interpolated between adjacent local light fields while keeping the illumination in the intermediate points coherent.

中文翻译：

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用于快速预计算全局照明的球面高斯光场纹理

我们描述了一种使用具有自由方向和任意锐度和幅度的球面高斯来近似场景中表面上任何点的预先计算的局部光场的方法。这允许以一种可用于实时渲染具有预先计算的全局照明的表面的方式对这些光场进行高质量的重建，并且内存和性能成本都非常低。我们还扩展了这个概念来表示光照加权的环境可见性，允许在考虑表面材料特性和可见性的情况下对远处环境进行高质量的反射。我们将获得球面高斯曲线视为一个优化问题，为此我们训练卷积神经网络为每个球面高斯参数生成合适的值。