A Bayesian Inference Framework for Procedural Material Parameter Estimation

Simple item page
dc.contributor.authorGuo, Yuen_US
dc.contributor.authorHasan, Milosen_US
dc.contributor.authorYan, Lingqien_US
dc.contributor.authorZhao, Shuangen_US
dc.contributor.editorEisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lueen_US
dc.date.accessioned2020-10-29T18:50:53Z
dc.date.available2020-10-29T18:50:53Z
dc.date.issued2020
dc.description.abstractProcedural 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.en_US
dc.description.number7
dc.description.sectionheadersMaterials and Shading Models
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume39
dc.identifier.doi10.1111/cgf.14142
dc.identifier.issn1467-8659
dc.identifier.pages255-266
dc.identifier.urihttps://doi.org/10.1111/cgf.14142
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14142
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectRendering
dc.titleA Bayesian Inference Framework for Procedural Material Parameter Estimationen_US
Files
Collections
39-Issue 7