A Unified Manifold Framework for Efficient BRDF Sampling based on Parametric Mixture Models
A Unified Manifold Framework for Efficient BRDF Sampling based on Parametric Mixture Models
复制标题
基于参数混合模型的高效 BRDF 采样统一流形框架
DOI：
10.2312/sre.20181171
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
H. Lensch
中科院分区：
文献类型：
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作者：
Sebastian Herholz;Oskar Elek;Jens Schindel;Jaroslav Křivánek;H. Lensch
几乎所有现有的解析BRDF模型都是由多个功能组件(例如，菲涅耳项、正态分布函数等)构建的。这使得对整个模型进行准确的重要性采样具有挑战性，因此当前的解决方案仅涵盖模型组件的子集。这会导致建议的方向样本不是最优的，甚至是无效的，这可能会对基于蒙特卡罗积分的光传输解算器的效率产生负面影响。为了克服这个问题，我们提出了一种基于参数混合模型(PMM)的统一BRDF抽样策略。我们证明了对于给定的BRDF，相关PMM的参数可以定义在光滑流形空间中，并且可以用多元B-样条来紧凑地表示。这些流形定义在BRDF的参数空间中，并允许针对变化的BRDF参数对PMM表示进行任意、连续的查询，这进一步使得能够对空间变化的BRDF进行重要性采样。我们的表示不仅限于解析的BRDF模型，还可以用于对测量的BRDF数据进行采样。由此得到的流形框架能够以非常小的逼近误差实现准确和高效的BRDF重要性采样。
Virtually all existing analytic BRDF models are built from multiple functional components (e.g., Fresnel term, normal distribution function, etc.). This makes accurate importance sampling of the full model challenging, and so current solutions only cover a subset of the model's components. This leads to sub-optimal or even invalid proposed directional samples, which can negatively impact the efficiency of light transport solvers based on Monte Carlo integration. To overcome this problem, we propose a unified BRDF sampling strategy based on parametric mixture models (PMMs). We show that for a given BRDF, the parameters of the associated PMM can be defined in smooth manifold spaces, which can be compactly represented using multivariate B-Splines. These manifolds are defined in the parameter space of the BRDF and allow for arbitrary, continuous queries of the PMM representation for varying BRDF parameters, which further enables importance sampling for spatially varying BRDFs. Our representation is not limited to analytic BRDF models, but can also be used for sampling measured BRDF data. The resulting manifold framework enables accurate and efficient BRDF importance sampling with very small approximation errors.
DOI：
10.1145/2601097.2601203
发表时间：
2014-07
期刊：
ACM Transactions on Graphics (TOG)
影响因子：
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作者：
J. Vorba;Ondrej Karlik;M. Šik;Tobias Ritschel;Jaroslav Křivánek
通讯作者：
J. Vorba;Ondrej Karlik;M. Šik;Tobias Ritschel;Jaroslav Křivánek