Several fast global illumination algorithms rely on the Virtual Point Lights framework. This framework separates illumination into two steps: first, propagate radiance in the scene and store it in virtual lights, then gather illumination from these virtual lights. To accelerate the second step, virtual lights and receiving points are grouped hierarchically, for example using Multi‐Dimensional Lightcuts. Computing visibility between clusters of virtual lights and receiving points is a bottleneck. Separately, matrix completion algorithms reconstruct completely a low‐rank matrix from an incomplete set of sampled elements. In this paper, we use adaptive matrix completion to approximate visibility information after an initial clustering step. We reconstruct visibility information using as little as 10 % to 20 % samples for most scenes, and combine it with shading information computed separately, in parallel on the GPU. Overall, our method computes global illumination 3 or more times faster than previous state‐of‐the‐art methods.

中文翻译：

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自适应矩阵补全，用于多光渲染的快速可见性计算

几种快速全局照明算法依赖于虚拟点光源框架。该框架将照明分为两个步骤：首先，在场景中传播辐射并将其存储在虚拟灯中，然后从这些虚拟灯中收集照明。为了加速第二步，虚拟灯和接收点按层次分组，例如使用多维光切。计算虚拟灯簇和接收点之间的可见性是一个瓶颈。另外，矩阵补全算法从一组不完整的采样元素中完全重建一个低秩矩阵。在本文中，我们在初始聚类步骤后使用自适应矩阵完成来近似可见性信息。对于大多数场景，我们使用低至 10% 到 20% 的样本重建可见性信息，并将其与单独计算的着色信息结合起来，在 GPU 上并行计算。总体而言，我们的方法计算全局照明的速度比以前最先进的方法快 3 倍或更多。