  In this work, we present and study a generalized family of differentiable
renderers. We discuss from scratch which components are necessary for
differentiable rendering and formalize the requirements for each component. We
instantiate our general differentiable renderer, which generalizes existing
differentiable renderers like SoftRas and DIB-R, with an array of different
smoothing distributions to cover a large spectrum of reasonable settings. We
evaluate an array of differentiable renderer instantiations on the popular
ShapeNet 3D reconstruction benchmark and analyze the implications of our
results. Surprisingly, the simple uniform distribution yields the best overall
results when averaged over 13 classes; in general, however, the optimal choice
of distribution heavily depends on the task.
