Current differentiable renderers provide light transport gradients with respect to arbitrary scene parameters. However, the mere existence of these gradients does not guarantee useful update steps in an optimization. Instead, inverse rendering might not converge due to inherent plateaus, i.e., regions of zero gradient, in the objective function. We propose to alleviate this by convolving the high-dimensional rendering function that maps scene parameters to images with an additional kernel that blurs the parameter space. We describe two Monte Carlo estimators to compute plateau-free gradients efficiently, i.e., with low variance, and show that these translate into net-gains in optimization error and runtime performance. Our approach is a straightforward extension to both black-box and differentiable renderers and enables optimization of problems with intricate light transport, such as caustics or global illumination, that existing differentiable renderers do not converge on.
翻译:当前的可微分渲染器可以基于场景参数提供光传输的梯度。然而,这些梯度的存在并不能保证能够通过优化算法得到有用的更新步骤,因为逆向渲染可能因为目标函数内部的高原(即零梯度区域)而无法收敛。本文提出了通过将高维度的渲染函数(其将场景参数映射为图像)与另一个卷积核进行卷积来缓解该问题的方法。我们提出了两种蒙特卡罗估计器以备快速且低方差地计算无高原的梯度,并证明这些方法能够更有效地减少优化误差和优化运行时间。我们的方法是黑盒可微分渲染器的直观扩展,它可以实现包含复杂光传输的问题的优化,如焦散或全局光照,这是现有可微分渲染器所无法收敛的。