Stochastic Gradient Estimation for Higher-order Differentiable Rendering

Fuente: arXiv
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Auteurs principaux: Wang, Zican, Fischer, Michael, Ritschel, Tobias
Format: Preprint
Publié: 2024
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author Wang, Zican
Fischer, Michael
Ritschel, Tobias
author_facet Wang, Zican
Fischer, Michael
Ritschel, Tobias
contents We derive methods to compute higher order differentials (Hessians and Hessian-vector products) of the rendering operator. Our approach is based on importance sampling of a convolution that represents the differentials of rendering parameters and shows to be applicable to both rasterization and path tracing. We further suggest an aggregate sampling strategy to importance-sample multiple dimensions of one convolution kernel simultaneously. We demonstrate that this information improves convergence when used in higher-order optimizers such as Newton or Conjugate Gradient relative to a gradient descent baseline in several inverse rendering tasks.
format Preprint
id arxiv_https___arxiv_org_abs_2412_03489
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Stochastic Gradient Estimation for Higher-order Differentiable Rendering
Wang, Zican
Fischer, Michael
Ritschel, Tobias
Graphics
We derive methods to compute higher order differentials (Hessians and Hessian-vector products) of the rendering operator. Our approach is based on importance sampling of a convolution that represents the differentials of rendering parameters and shows to be applicable to both rasterization and path tracing. We further suggest an aggregate sampling strategy to importance-sample multiple dimensions of one convolution kernel simultaneously. We demonstrate that this information improves convergence when used in higher-order optimizers such as Newton or Conjugate Gradient relative to a gradient descent baseline in several inverse rendering tasks.
title Stochastic Gradient Estimation for Higher-order Differentiable Rendering
topic Graphics
url https://arxiv.org/abs/2412.03489