Probabilistic illumination-aware filtering for Monte Carlo rendering
Probabilistic illumination-aware filtering for Monte Carlo rendering
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DOI：
10.1007/s00371-013-0807-3
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Ian C. Doidge;Mark W. Jones
中科院分区：
文献类型：
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作者：
Ian C. Doidge;Mark W. Jones
蒙特卡罗全局光照渲染中的噪声去除是一个众所周知的问题，基于图像的滤波方法引起了人们的极大关注。然而，许多最先进的方法在高频特征、复杂的照明和材料的存在下崩溃。在这项工作中，我们提出了一种基于概率图像的噪声去除和辐照度过滤框架，该框架保留了这种高频细节，如硬阴影和光泽反射，并且不对光传输或材料的特性施加限制。我们维护路径跟踪样本的每像素簇，并使用来自这些簇的统计数据，基于离散泊松概率分布推导出一种光照感知过滤方案。此外，我们对样本的入射辐射进行了过滤，允许我们在不限制过滤器的有效性的情况下保留和过滤高频和复杂的纹理。
Noise removal for Monte Carlo global illumination rendering is a well known problem, and has seen significant attention from image-based filtering methods. However, many state of the art methods breakdown in the presence of high frequency features, complex lighting and materials. In this work we present a probabilistic image based noise removal and irradiance filtering framework that preserves this high frequency detail such as hard shadows and glossy reflections, and imposes no restrictions on the characteristics of the light transport or materials. We maintain per-pixel clusters of the path traced samples and, using statistics from these clusters, derive an illumination aware filtering scheme based on the discrete Poisson probability distribution. Furthermore, we filter the incident radiance of the samples, allowing us to preserve and filter across high frequency and complex textures without limiting the effectiveness of the filter.