  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.
