Selective guided sampling with complete light transport paths
Selective guided sampling with complete light transport paths
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DOI：
10.1145/3272127.3275030
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发表时间：
2018-12
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影响因子：
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通讯作者：
Florian Reibold;J. Hanika;Alisa Jung;C. Dachsbacher
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作者：
Florian Reibold;J. Hanika;Alisa Jung;C. Dachsbacher
为蒙特卡罗光传输找到良好的全球重要性抽样策略是具有挑战性的。虽然使用局部方法(如BSDF采样或下一事件估计)的估计器通常在场景的大部分区域中工作得很好，但路径空间中的小区域可能采样不足(例如，反射焦散)。我们提出了一种新的数据驱动的引导抽样方法，它选择性地适应于这些有问题的区域，并补充了非引导估计器。它基于完整的传输路径，即能够解决由于BSDF和参与介质中的自由飞行距离造成的关联。它的概念很简单，在引导路径周围放置各向异性截断的高斯分布，以重建连续的概率密度函数(引导PDF)。从引导的以及非引导的PDF迭代地采样引导路径，并且仅当它们在当前估计器中引起高方差时才记录它们。当普通的蒙特卡罗方法独立地对路径进行采样，而基于马尔可夫链的方法扰动单个当前样本时，我们通过一组相邻路径来确定重构核。这使得对被积函数的局部探索不需要详细的平衡约束或需要解析导数。我们证明了我们的方法可以将路径空间分解成由非引导估计器很好地采样的区域和由新的引导采样器处理的区域。在现实场景中，我们显示的加速比是无引导采样器的4倍。
Finding good global importance sampling strategies for Monte Carlo light transport is challenging. While estimators using local methods (such as BSDF sampling or next event estimation) often work well in the majority of a scene, small regions in path space can be sampled insufficiently (e.g. a reflected caustic). We propose a novel data-driven guided sampling method which selectively adapts to such problematic regions and complements the unguided estimator. It is based on complete transport paths, i.e. is able to resolve the correlation due to BSDFs and free flight distances in participating media. It is conceptually simple and places anisotropic truncated Gaussian distributions around guide paths to reconstruct a continuous probability density function (guided PDF). Guide paths are iteratively sampled from the guided as well as the unguided PDF and only recorded if they cause high variance in the current estimator. While plain Monte Carlo samples paths independently and Markov chain-based methods perturb a single current sample, we determine the reconstruction kernels by a set of neighbouring paths. This enables local exploration of the integrand without detailed balance constraints or the need for analytic derivatives. We show that our method can decompose the path space into a region that is well sampled by the unguided estimator and one that is handled by the new guided sampler. In realistic scenarios, we show 4× speedups over the unguided sampler.