"Estimating Homogeneous Data-driven BRDF Parameters from a Reflectance Map under Known Natural Lighting"
Victoria Cooper, James Bieron, and Pieter Peers

IEEE Transactions on Visualization and Computer Graphics, June 2021
Abstract
In 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. We formulate the estimation of the model parameters as a non-linear maximum a-posteriori optimization, and introduce a linear approximation that estimates a solution per material class from which the best solution is selected. We demonstrate the efficacy and robustness of our method using the MERL BRDF database under a variety of natural lighting conditions, and we provide a proof-of-concept real-world experiment.


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Related Publications
  • Victoria L. Cooper, James C. Bieron, and Pieter Peers, "Estimating Homogeneous Data-driven BRDF Parameters from a Reflectance Map under Known Natural Lighting", Eurographics Workshop on Material Appearance Modeling, June 2019,
  • Victoria L. Cooper, James C. Bieron, and Pieter Peers, "Estimating Homogeneous Data-driven BRDF Parameters from a Reflectance Map under Known Natural Lighting", CoRR, abs/1906.04777, June 2019,
Bibtex
@article{Cooper:2021:EHD,
author = {Cooper, Victoria and Bieron, James and Peers, Pieter},
title = {Estimating Homogeneous Data-driven {BRDF} Parameters from a Reflectance Map under Known Natural Lighting},
month = {June},
year = {2021},
journal = {IEEE Transactions on Visualization and Computer Graphics},
doi = {https://doi.org/10.1109/TVCG.2021.3085560},
}