BTF Modelling Using BRDF Texels (original) (raw)

Efficient Image-Based Bidirectional Texture Function Model

2005

Recent advances in computer hardware and virtual mod- elling allow to respect view and illumination dependencies of natural surface materials. The corresponding texture representation in the form of Bidirectional Texture Func- tion (BTF) enables significant improvements of virtual mod- els realism at the expense of immense increase of material sample data space. Thus introduction of some fast com- pression, modelling and rendering method for BTF data is inevitable. In this paper we introduce a generalisation of our polynomial extension of the Lafortune model computed for every original BTF measurement pixel allowing seam- less BTF texture enlargement. This nonlinear reflectance model is further extended using parameters clustering tech- nique to achieve higher compression ratio. The presented method offers BTF modelling in excellent visual quality as was tested on variety of BTF measurements. The method gives BTF compression ratio 1:200 as well as fast graph- ics hardware implementa...

A Survey of BRDF Models for Computer Graphics

To produce photo-realistic images in computer graphics, we must effectively describe the interactions between light and surfaces. In this paper, we focus on Bidirectional Reflectance Distribution Functions (BRDFs), which characterize these interactions. We survey on most BRDF representations introduced so far and we investigate their usage, importance and applications. We look at in detail their two important usages; in GPU-based real-time renderings and in renderings of metallic car paints.

A General BRDF Representation Based on Tensor Decomposition

Generating 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.