Monte Carlo积分的几何样本加权
Geometric Sample Reweighting for Monte Carlo Integration
摘要：
我们提出了一个通用的样本加权计划和其基本理论的集成的未知函数的低维。我们的方法产生更好的结果比标准的加权方案为常见的采样策略，同时避免偏见。我们的主要见解是在积分过程中将权重推导与函数重构过程联系起来。我们的解决方案的实现很简单，并且可以改善收敛行为。我们说明了它的好处，我们的方法应用到多个蒙特卡洛渲染问题。
英文摘要：
We present a general sample reweighting scheme and its underlying theory for the integration of an unknown function with low dimensionality. Our method produces better results than standard weighting schemes for common sampling strategies, while avoiding bias. Our main insight is to link the weight derivation to the function reconstruction process during integration. The implementation of our solution is simple and results in an improved convergence behavior. We illustrate its benefit by applying our method to multiple Monte Carlo rendering problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
