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：当前的可微渲染器可提供与任意场景参数相关的光传输梯度。然而，这些梯度的存在并不能保证优化过程中能产生有效的更新步长。相反，由于目标函数中固有的平台区域（即梯度为零的区域），逆渲染可能无法收敛。我们提出通过将场景参数映射到图像的高维渲染函数与一个额外的模糊参数空间的核进行卷积来缓解这一问题。我们设计了两种蒙特卡洛估计器，以高效（即低方差）计算无平台效应的梯度，并证明这些方法能转化为优化误差和运行时性能的净收益。我们的方法是对黑盒渲染器和可微渲染器的直接扩展，能够优化涉及复杂光传输（如焦散或全局光照）的问题，而现有可微渲染器对此类问题无法收敛。