A Spherical Gaussian Framework for Bayesian Monte Carlo Rendering of Glossy Surfaces
A Spherical Gaussian Framework for Bayesian Monte Carlo Rendering of Glossy Surfaces
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DOI：
10.1109/tvcg.2013.79
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发表时间：
2013-05
影响因子：
5.2
通讯作者：
C. Bouville;Mickaël Ribardière;Luís Paulo Santos;K. Bouatouch;R. Marques
中科院分区：
文献类型：
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作者：
C. Bouville;Mickaël Ribardière;Luís Paulo Santos;K. Bouatouch;R. Marques
蒙特卡罗方法已被证明在处理全局照明问题方面非常强大，但就采样操作而言，它仍然代价高昂。在各种应用中，前人的工作已经表明，由于贝叶斯蒙特卡罗能够更有效地利用先验知识和样本集带来的信息，因此贝叶斯蒙特卡罗的性能明显优于重要性抽样蒙特卡罗。这些良好的结果已经在全局照明的上下文中得到了确认，但严格限制在完美漫反射的情况下。我们在本文中的主要目标是提出一个更通用的贝叶斯蒙特卡罗解决方案，它可以处理基于球面高斯的框架下的非漫射BRDF。我们还提出了一种快速确定超参数的方法，该方法避免了学习每个BRDF的超参数。这些贡献代表了将贝叶斯蒙特卡罗推广到全局照明渲染的两个主要步骤。我们表明，与重要性抽样相比，我们在相当的计算成本下获得了实质性的质量改进。
The Monte Carlo method has proved to be very powerful to cope with global illumination problems but it remains costly in terms of sampling operations. In various applications, previous work has shown that Bayesian Monte Carlo can significantly outperform importance sampling Monte Carlo thanks to a more effective use of the prior knowledge and of the information brought by the samples set. These good results have been confirmed in the context of global illumination but strictly limited to the perfect diffuse case. Our main goal in this paper is to propose a more general Bayesian Monte Carlo solution that allows dealing with nondiffuse BRDFs thanks to a spherical Gaussian-based framework. We also propose a fast hyperparameters determination method that avoids learning the hyperparameters for each BRDF. These contributions represent two major steps toward generalizing Bayesian Monte Carlo for global illumination rendering. We show that we achieve substantial quality improvements over importance sampling at comparable computational cost.