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arXiv · 2211.13333

Learning to Rasterize Differentiably

Chenghao Wu·Hamila Mailee·Zahra Montazeri·Tobias Ritschel
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Abstract

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.

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Chenghao Wu, Hamila Mailee, Zahra Montazeri, Tobias Ritschel. 2022-11-23. Learning to Rasterize Differentiably. https://doi.org/10.1111/cgf.15145

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