Adaptive numerical cumulative distribution functions for efficient importance sampling
Adaptive numerical cumulative distribution functions for efficient importance sampling
复制标题
用于高效重要性采样的自适应数值累积分布函数
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
R. Ramamoorthi
中科院分区：
文献类型：
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作者：
Jason Lawrence;S. Rusinkiewicz;R. Ramamoorthi
随着基于图像的表面反射率和照明在基于物理的渲染系统中得到更广泛的使用，提供允许根据这些高维测量函数中的能量分布对光路进行采样的表示变得更加关键。在本文中，我们应用传统上用于曲线近似的算法，以减少一个多维列表的累积分布函数（CDF）的大小由一至三个数量级，而不影响其保真度。这些自适应表示使新的算法，根据表面的局部方向采样环境地图和基于图像的照明和测量的BRDF的多个重要性采样。
As image-based surface reflectance and illumination gain wider use in physically-based rendering systems, it is becoming more critical to provide representations that allow sampling light paths according to the distribution of energy in these high-dimensional measured functions. In this paper, we apply algorithms traditionally used for curve approximation to reduce the size of a multidimensional tabulated Cumulative Distribution Function (CDF) by one to three orders of magnitude without compromising its fidelity. These adaptive representations enable new algorithms for sampling environment maps according to the local orientation of the surface and for multiple importance sampling of image-based lighting and measured BRDFs.