Path replay backpropagation
Path replay backpropagation
复制标题
路径重放反向传播
DOI：
10.1145/3476576.3476672
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Wenzel Jakob
中科院分区：
文献类型：
--
作者：
Delio Vicini;Sébastien Speierer;Wenzel Jakob
可微的基于物理的绘制已经成为解决涉及光的逆问题的不可或缺的工具。在这一领域的大多数应用程序联合优化一个大的场景参数集，以最小化的目标函数，在这种情况下，反向模式分化是获得参数梯度的方法的选择。然而，执行必要的微分步骤的现有技术遭受统计偏差或在存储器和计算时间方面的过高成本。例如，基于程序转换或Wengert磁带的自动区分的标准技术在应用于基于物理的渲染算法时会导致不切实际的大内存使用。Nimier-David等人最近提出的伴随方法。[2020]将其减少到恒定的内存占用，但无偏梯度估计的计算时间则成为沿光路沿着散射事件数量的二次函数。当场景包含高度散射的材料（如参与介质）时，这是有问题的。在本文中，我们提出了一种新的无偏反向传播算法的渲染，只需要恒定的内存，其计算时间是线性的散射事件的数量（即，就像路径跟踪一样）。我们的方法建立在散射相互作用的局部雅可比矩阵的可逆性上，以恢复反向模式微分所需的各种数量。我们的方法还扩展到镜面材料，如光滑的玻璃和导体，不能通过先前的工作处理。
Differentiable physically-based rendering has become an indispensable tool for solving inverse problems involving light. Most applications in this area jointly optimize a large set of scene parameters to minimize an objective function, in which case reverse-mode differentiation is the method of choice for obtaining parameter gradients. However, existing techniques that perform the necessary differentiation step suffer from either statistical bias or a prohibitive cost in terms of memory and computation time. For example, standard techniques for automatic differentiation based on program transformation or Wengert tapes lead to impracticably large memory usage when applied to physically-based rendering algorithms. A recently proposed adjoint method by Nimier-David et al. [2020] reduces this to a constant memory footprint, but the computation time for unbiased gradient estimates then becomes quadratic in the number of scattering events along a light path. This is problematic when the scene contains highly scattering materials like participating media. In this paper, we propose a new unbiased backpropagation algorithm for rendering that only requires constant memory, and whose computation time is linear in the number of scattering events (i.e., just like path tracing). Our approach builds on the invertibility of the local Jacobian at scattering interactions to recover the various quantities needed for reverse-mode differentiation. Our method also extends to specular materials such as smooth dielectrics and conductors that cannot be handled by prior work.
可解释的多项式神经常微分方程。
DOI：
10.1063/5.0130803
发表时间：
2023
期刊：
Chaos (Woodbury, N.Y.)
影响因子：
--
作者：
Fronk,Colby;Petzold,Linda
通讯作者：
Petzold,Linda