Learning to Rasterize Differentiably

Differentiable rasterization changes the standard formulation of primitive rasterization —by enabling gradient flow from a
pixel to its underlying triangles— using distribution functions in different stages of rendering, creating a “soft” version of
the original rasterizer. However, choosing the optimal softening function that ensures the best performance and convergence
to a desired goal requires trial and error. Previous work has analyzed and compared several combinations of softening. In
this work, we take it a step further and, instead of making a combinatorial choice of softening operations, parameterize the
continuous space of common softening operations. We study meta-learning tunable softness functions over a set of inverse
rendering tasks (2D and 3D shape, pose and occlusion) so it generalizes to new and unseen differentiable rendering tasks with
optimal softness.