Mixing Monte Carlo and progressive rendering for improved global illumination
Mixing Monte Carlo and progressive rendering for improved global illumination
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DOI：
10.1007/s00371-012-0703-2
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发表时间：
2012-04
期刊：
影响因子：
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通讯作者：
Ian C. Doidge;Mark W. Jones;Benjamin Mora
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文献类型：
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作者：
Ian C. Doidge;Mark W. Jones;Benjamin Mora
在本文中，我们试图消除渐进蒙特卡罗路径跟踪过程中的焦散路径所造成的噪声。我们在路径空间上采用过滤策略，使用路径跟踪和渐进光子映射的专门推导来处理每个子空间。使用路径跟踪评估漫射路径允许在像素和整个图像上使用样本分层，同时使用渐进光子映射产生清晰详细的焦散。这是一个有效的，低噪声渐进算法与消失的偏见相结合的优势，蒙特卡罗方法和粒子跟踪。
In this paper, we seek to eliminate the noise caused by caustic paths during progressive Monte Carlo path tracing. We employ a filtering strategy over path space, handling each subspace using specialized derivations of path tracing and progressive photon mapping. Evaluating diffuse paths with path tracing allows the use of sample stratification over both pixels and the image as a whole, whilst sharp detailed caustics are produced using progressive photon mapping. This is an efficient, low noise progressive algorithm with vanishing bias combining the advantages of both Monte Carlo methods, and particle tracing.