Ahmet BilgiliAydin ÖztürkMurat Kurt2025-10-06201114678659, 016770550167-70551467-865910.1111/j.1467-8659.2011.02072.xhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84872221376&doi=10.1111%2Fj.1467-8659.2011.02072.x&partnerID=40&md5=c38724918ddf4639127dcca0f0f3426bhttps://gcris.yasar.edu.tr/handle/123456789/10249Generating photo-realistic images through Monte Carlo rendering requires efficient representation of light-surface interaction and techniques for importance sampling. Various models with good representation abilities have been developed but only a few of them have their importance sampling procedure. In this paper we propose a method which provides a good bidirectional reflectance distribution function (BRDF) representation and efficient importance sampling procedure. Our method is based on representing BRDF as a function of tensor products. Four-dimensional measured BRDF tensor data are factorized using Tucker decomposition. A large data set is used for comparing the proposed BRDF model with a number of well-known BRDF models. It is shown that the underlying model provides good approximation to BRDFs. © 2011 The Authors. © 2018 Elsevier B.V. All rights reserved.EnglishBrdf Representation, Global Illumination, Importance Sampling, Rendering, Tucker Decomposition, Distribution Functions, Monte Carlo Methods, Rendering (computer Graphics), Tensors, Bidirectional Reflectance Distribution Functions, Brdf Representation, Efficient Importance Samplings, Global Illumination, Monte-carlo Rendering, Photorealistic Images, Rendering, Tucker Decompositions, Importance SamplingDistribution functions, Monte Carlo methods, Rendering (computer graphics), Tensors, Bidirectional reflectance distribution functions, BRDF representation, Efficient importance samplings, Global illumination, Monte-Carlo rendering, Photorealistic images, Rendering, Tucker decompositions, Importance samplingArticle