Improved Stochastic Progressive Photon Mapping with Metropolis Sampling

Improved Stochastic Progressive Photon Mapping with Metropolis Sampling
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使用 Metropolis 采样改进随机渐进光子映射

DOI:
10.1111/j.1467-8659.2011.01979.x
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发表时间:
2011-06
影响因子:
2.5
通讯作者:
Yong, Jun-Hai
Yong, Jun-Hai
中科院分区:
计算机科学4区
文献类型:
--
作者:
Chen, Jiating;Wang, Bin;Yong, Jun-Hai

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本文对随机渐进光子映射(SPPM)进行了改进,SPPM是一种具有分布式光线追踪效果的复杂全局照明鲁棒模拟方法。通常,与光子映射和其他粒子跟踪算法类似,当光子分布不佳时,SPPM会变得低效。为了将噪声和偏置引起的误差降低到可接受的水平,需要大量的光子。为了优化光子的分布,我们提出了一种扩展SPPM的Metropolis - Hastings算法,有效地利用了促成渲染图像的光路之间的局部相干性。在Metropolis采样策略中引入了一个精心设计的标量贡献函数,针对图像区域中存在较大误差的特定部分,提高了辐射估计器的效率。实验结果表明,基于Metropolis采样的新方法在保持标准SPPM方法鲁棒性的同时,显著提高了大范围复杂光照场景的渲染效率。
This paper presents an improvement to the stochastic progressive photon mapping (SPPM), a method for robustly simulating complex global illumination with distributed ray tracing effects. Normally, similar to photon mapping and other particle tracing algorithms, SPPM would become inefficient when the photons are poorly distributed. An inordinate amount of photons are required to reduce the error caused by noise and bias to acceptable levels. In order to optimize the distribution of photons, we propose an extension of SPPM with a Metropolis‐Hastings algorithm, effectively exploiting local coherence among the light paths that contribute to the rendered image. A well‐designed scalar contribution function is introduced as our Metropolis sampling strategy, targeting at specific parts of image areas with large error to improve the efficiency of the radiance estimator. Experimental results demonstrate that the new Metropolis sampling based approach maintains the robustness of the standard SPPM method, while significantly improving the rendering efficiency for a wide range of scenes with complex lighting.
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