A Bayesian Inference Framework for
Procedural Material Parameter Estimation
Yu Guo1, Miloš Hašan2, Lingqi Yan3, and Shuang Zhao1
1University of California, Irvine     2Adobe Research     3University of California, Santa Barbara
Computer Graphics Forum (Pacific Graphics), 39(7), December 2020

Procedural material models have been gaining traction in many applications thanks to their flexibility, compactness, and easy editability. In this paper, we explore the inverse rendering problem of procedural material parameter estimation from photographs using a Bayesian framework. We use summary functions for comparing unregistered images of a material under known lighting, and we explore both hand-designed and neural summary functions. In addition to estimating the parameters by optimization, we introduce a Bayesian inference approach using Hamiltonian Monte Carlo to sample the space of plausible material parameters, providing additional insight into the structure of the solution space. To demonstrate the effectiveness of our techniques, we fit procedural models of a range of materials---wall plaster, leather, wood, anisotropic brushed metals and metallic paints---to both synthetic and real target images.

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Bibtex Citation
  title={A Bayesian Inference Framework for Procedural Material Parameter Estimation},
  author={Guo, Yu and Ha\v{s}an, Milo\v{s} and Yan, Lingqi and Zhao, Shuang},
  journal={Computer Graphics Forum},