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