A Bayesian Inference Framework for Procedural Material Parameter Estimation

Abstract

Procedural material models have been gaining traction in many applications thanks to their flexibility, compactness, and easy editability. We explore the inverse rendering problem of procedural material parameter estimation from photographs, presenting a unified view of the problem in a Bayesian framework. In addition to computing point estimates of the parameters by optimization, our framework uses a Markov Chain Monte Carlo approach to sample the space of plausible material parameters, providing a collection of plausible matches that a user can choose from, and efficiently handling both discrete and continuous model parameters. To demonstrate the effectiveness of our framework, we fit procedural models of a range of materials---wall plaster, leather, wood, anisotropic brushed metals and layered metallic paints---to both synthetic and real target images.

12 pages, 13 figures, Pacific Graphics 2020

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses

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• Node Graph Optimization Using Differentiable Proxies
• UMat: Uncertainty-Aware Single Image High Resolution Material Capture