Continuous multiple importance sampling
Continuous multiple importance sampling
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DOI：
10.1145/3386569.3392436
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Rex West;Iliyan Georgiev;Adrien Gruson;T. Hachisuka
中科院分区：
文献类型：
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作者：
Rex West;Iliyan Georgiev;Adrien Gruson;T. Hachisuka
在蒙特卡罗积分估计中，多重重要抽样（MIS）是一种结合有限采样技术来减小方差的好方法。然而，存在一个连续采样技术可用的积分问题。为了处理这种情况，我们建立了一个连续的管理信息系统（CMIS）公式，作为管理信息系统的推广到无数个技术集合。我们的公式配备了一个基本估计器，它与一个可证明的最优平衡启发式和一个实用的随机MIS （SMIS）估计器相结合，使CMIS可用于广泛的问题。为了说明我们的框架的有效性和实用性，我们将其应用于三种不同的轻运输应用，显示出比先前最先进的技术更好的性能。
Multiple importance sampling (MIS) is a provably good way to combine a finite set of sampling techniques to reduce variance in Monte Carlo integral estimation. However, there exist integration problems for which a continuum of sampling techniques is available. To handle such cases we establish a continuous MIS (CMIS) formulation as a generalization of MIS to uncountably infinite sets of techniques. Our formulation is equipped with a base estimator that is coupled with a provably optimal balance heuristic and a practical stochastic MIS (SMIS) estimator that makes CMIS accessible to a broad range of problems. To illustrate the effectiveness and utility of our framework, we apply it to three different light transport applications, showing improved performance over the prior state-of-the-art techniques.