论文标题
在线路径采样控制和进行性时空过滤
Online path sampling control with progressive spatio-temporal filtering
论文作者
论文摘要
这项工作引入了渐进时空过滤，这是一种有效的方法，是通过在线，迭代算法和数据结构中滤除基础路径采样器产生的单个样本，从而将光传输分布构建到场景中的全频近似，从而利用了近似光字段的空间和时间一致性。与以前的方法不同，由于使用迭代的时间反馈循环，该方法既更有效，又可以大大提高与无噪声近似物的收敛性，并且更加灵活，因为它引入了空间方向的哈希表示形式，该表示允许通过景点进行编码的方向变化，例如由于光泽的反射而进行编码。然后，我们引入了四种不同的方法来利用所得近似来控制基础路径采样器和/或修改其相关的估计器，从而大大降低了其方差并增强其鲁棒性，以使其对复杂的照明场景。核心算法是高度可扩展的且低空的，仅需要对现有路径示踪剂进行少量修改。
This work introduces progressive spatio-temporal filtering, an efficient method to build all-frequency approximations to the light transport distribution into a scene by filtering individual samples produced by an underlying path sampler, using online, iterative algorithms and data-structures that exploit both the spatial and temporal coherence of the approximated light field. Unlike previous approaches, the proposed method is both more efficient, due to its use of an iterative temporal feedback loop that massively improves convergence to a noise-free approximant, and more flexible, due to its introduction of a spatio-directional hashing representation that allows to encode directional variations like those due to glossy reflections. We then introduce four different methods to employ the resulting approximations to control the underlying path sampler and/or modify its associated estimator, greatly reducing its variance and enhancing its robustness to complex lighting scenarios. The core algorithms are highly scalable and low-overhead, requiring only minor modifications to an existing path tracer.
