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Primary-space Adaptive Control Variates Using Piecewise-polynomial Approximations
ACM Transactions on Graphics  ( IF 6.2 ) Pub Date : 2021-07-15 , DOI: 10.1145/3450627
Miguel Crespo 1 , Adrian Jarabo 2 , Adolfo Muñoz 3
Affiliation  

We present an unbiased numerical integration algorithm that handles both low-frequency regions and high-frequency details of multidimensional integrals. It combines quadrature and Monte Carlo integration by using a quadrature-based approximation as a control variate of the signal. We adaptively build the control variate constructed as a piecewise polynomial, which can be analytically integrated, and accurately reconstructs the low-frequency regions of the integrand. We then recover the high-frequency details missed by the control variate by using Monte Carlo integration of the residual. Our work leverages importance sampling techniques by working in primary space, allowing the combination of multiple mappings; this enables multiple importance sampling in quadrature-based integration. Our algorithm is generic and can be applied to any complex multidimensional integral. We demonstrate its effectiveness with four applications with low dimensionality: transmittance estimation in heterogeneous participating media, low-order scattering in homogeneous media, direct illumination computation, and rendering of distribution effects. Finally, we show how our technique is extensible to integrands of higher dimensionality by computing the control variate on Monte Carlo estimates of the high-dimensional signal, and accounting for such additional dimensionality on the residual as well. In all cases, we show accurate results and faster convergence compared to previous approaches.

中文翻译:

使用分段多项式近似的主空间自适应控制变量

我们提出了一种无偏数值积分算法,可以处理多维积分的低频区域和高频细节。它通过使用基于正交的近似作为信号的控制变量来结合正交和蒙特卡罗积分。我们自适应地构建了构造为分段多项式的控制变量,可以进行分析积分,并准确地重建被积函数的低频区域。然后,我们通过使用残差的蒙特卡罗积分来恢复控制变量遗漏的高频细节。我们的工作通过在主要空间中工作来利用重要性采样技术,允许组合多个映射;这使得基于正交的集成中的多重重要性采样成为可能。我们的算法是通用的,可以应用于任何复杂的多维积分。我们通过四种低维应用证明了它的有效性:异质参与介质中的透射率估计、均质介质中的低阶散射、直接照明计算和分布效果的渲染。最后,我们展示了我们的技术如何通过计算高维信号的蒙特卡罗估计的控制变量,以及考虑残差上的这种附加维数,来扩展到更高维的被积函数。在所有情况下,与以前的方法相比,我们都显示出准确的结果和更快的收敛速度。均匀介质中的低阶散射、直接照明计算和分布效果的渲染。最后,我们展示了我们的技术如何通过计算高维信号的蒙特卡罗估计的控制变量,以及考虑残差上的这种附加维数,来扩展到更高维的被积函数。在所有情况下,与以前的方法相比,我们都显示出准确的结果和更快的收敛速度。均匀介质中的低阶散射、直接照明计算和分布效果的渲染。最后,我们展示了我们的技术如何通过计算高维信号的蒙特卡罗估计的控制变量,以及考虑残差上的这种附加维数,来扩展到更高维的被积函数。在所有情况下,与以前的方法相比,我们都显示出准确的结果和更快的收敛速度。
更新日期:2021-07-15
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