43-Issue 4

Permanent URI for this collection

Now showing 1 - 1 of 1

Learning to Rasterize Differentiably
(The Eurographics Association and John Wiley & Sons Ltd., 2024) Wu, Chenghao; Mailee, Hamila; Montazeri, Zahra; Ritschel, Tobias; Garces, Elena; Haines, Eric
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.