Show simple item record

dc.contributor.authorCooper, Victoria L.en_US
dc.contributor.authorBieron, James C.en_US
dc.contributor.authorPeers, Pieteren_US
dc.contributor.editorKlein, Reinhard and Rushmeier, Hollyen_US
dc.date.accessioned2019-09-11T09:10:51Z
dc.date.available2019-09-11T09:10:51Z
dc.date.issued2019
dc.identifier.isbn978-3-03868-080-2
dc.identifier.issn2309-5059
dc.identifier.urihttps://doi.org/10.2312/mam.20191308
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/mam20191308
dc.description.abstractIn this paper we demonstrate robust estimation of the model parameters of a fully-linear data-driven BRDF model from a reflectance map under known natural lighting. To regularize the estimation of the model parameters, we leverage the reflectance similarities within a material class. We approximate the space of homogeneous BRDFs using a Gaussian mixture model, and assign a material class to each Gaussian in the mixture model. Next, we compute a linear solution per material class. Finally, we select the best candidate as the final estimate. We demonstrate the efficacy and robustness of our method using the MERL BRDF database under a variety of natural lighting conditions.en_US
dc.publisherThe Eurographics Associationen_US
dc.subject!
dc.titleEstimating Homogeneous Data-driven BRDF Parameters from a Reflectance Map under Known Natural Lightingen_US
dc.description.seriesinformationWorkshop on Material Appearance Modeling
dc.description.sectionheadersModels, Fitting, and Measurement
dc.identifier.doi10.2312/mam.20191308
dc.identifier.pages23-26


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record