Anisotropic Bidirectional Reflectance Distribution Function Research Articles

Surface texture evaluation using 3D reconstruction from images by parametric anisotropic BRDF

We present a method for surface texture evaluation using machine vision by studying the phenomenon of reflection from a real surface. A parameterized anisotropic bi-directional reflectance distribution function (BRDF) is proposed along with a fusion reconstruction method which is analogous to the human visual system. The proposed reflection model operates on an image dataset to output the reconstructed shape. Machined surfaces obtained by performing mechanical grinding at varying machining conditions are analyzed using gray level co-occurrence matrix (GLCM), wavelet decomposition, photometric stereo and fusion reconstruction based texture analysis to study and benchmark performance of both statistical and topographical surface texture evaluation methods. The four methods are implemented in MATLAB™ to estimate surface roughness parameters – Ra, Rq, and Rz. Error analysis is performed by comparing estimated roughness values against stylus profilometer measurements. The comparison reveals that fusion reconstruction estimates surface roughness closer to stylus profilometer measurements as compared to GLCM, wavelet decomposition and photometric stereo based texture analysis.

Perceptually Motivated BRDF Comparison using Single Image

Surface reflectance of real-world materials is now widely represented by the bidirectional reflectance distribution function (BRDF) and also by spatially varying representations such as SVBRDF and the bidirectional texture function (BTF). The raw surface reflectance measurements are typically compressed or fitted by analytical models, that always introduce a certain loss of accuracy. For its evaluation we need a distance function between a reference surface reflectance and its approximate version. Although some of the past techniques tried to reflect the perceptual sensitivity of human vision, they have neither optimized illumination and viewing conditions nor surface shape. In this paper, we suggest a new image-based methodology for comparing different anisotropic BRDFs. We use optimization techniques to generate a novel surface which has extensive coverage of incoming and outgoing light directions, while preserving its features and frequencies that are important for material appearance judgments. A single rendered image of such a surface along with simultaneously optimized lighting and viewing directions leads to the computation of a meaningful BRDF difference, by means of standard image difference predictors. A psychophysical experiments revealed that our surface provides richer information on material properties than the standard surfaces often used in computer graphics, e.g., sphere or blob.

Reliable estimation of anisotropic BRDF

Bidirectional reflectance distribution function (BRDF) estimation is one of the fundamental problems in computer graphics, computer vision and related fields, yet most work relies on conventional local minimisation. Two global optimisation methods for estimating the anisotropic Ward BRDF model and its variants under an L 2-cost function are introduced. The presented algorithms are based on branch-and-bound search which guarantees to provide globally optimal solutions. In contrast to the traditional local optimisation, the methods mentioned here are not affected by the setting of initial BRDF parameter values. Experiments have been carried out to validate the presented methods using anisotropic materials from the UTIA BRDF database.

Adaptive highlights stencils for modeling of multi-axial BRDF anisotropy

Directionally dependent anisotropic material appearance phenomenon is widely represented using bidirectional reflectance distribution function (BRDF). This function needs in practice either reconstruction of unknown values interpolating between sparse measured samples or requires data fidelity preserving compression forming a compact representation from dense measurements. Both properties can be, to a certain extent, preserved by means of analytical BRDF models. Unfortunately, the number of anisotropic BRDF models is limited, and moreover, most require either a demanding iterative optimization procedure dependent on proper initialization or the user setting parameters. Most of these approaches are challenged by the fitting of complex anisotropic BRDFs. In contrast, we approximate BRDF anisotropic behavior by means of highlight stencils and derive a novel BRDF model that independently adapts such stencils to each anisotropic mode present in the BRDF. Our model allows for the fast direct fitting of parameters without the need of any demanding optimization. Furthermore, it achieves an encouraging, expressive visual quality as compared to rival solutions that rely on a similar number of parameters. We thereby ascertain that our method represents a promising approach to the analysis and modeling of complex anisotropic BRDF behavior.

An anisotropic BRDF model for fitting and Monte Carlo rendering

In this paper we propose a new physically plausible, anisotropic Bidirectional Reflectance Distribution Function (BRDF) for fitting and for importance sampling in global illumination rendering. We demonstrate that the new model is better in data fitting than existing BRDF models. We also describe efficient schemes for sampling the proposed anisotropic BRDF model. Furthermore, we test it on a GPU-based real-time rendering algorithm and show that material design can be done with this anisotropic BRDF model effectively. We also show that the new model has effective real-time rendering performance.

A Correction Algorithm for Distorted Laser Images

The reflectance of fabric surface is commonly represented by a bidirectional reflectance distribution function (BRDF). To generate the image-based BRDF from measured data, it requires an enormous amount of measurements. In this paper, we propose an efficient image-based method for rendering the anisotropic BRDF of silk-like woven fabrics based on the micro facet surface geometry determined by the cross-sectional shape of fibers, twist of yarns, and type of weave. We develop an image-based method for generating the BRDF of silk-like woven fabrics from measurement of the reflectances caused by the incident light only in the direction perpendicular to the fabric's surface.

BRDF Measurement Modelling using Wavelets for Efficient Path Tracing

Abstract Physically based rendering needs numerical models from real measurements, or analytical models from material definitions, of the Bidirectional Reflectance Distribution Function (BRDF). However, measured BRDF data sets are too large and provide no functionalities to be practically used in Monte Carlo path tracing algorithms. In this paper, we present a wavelet‐based generic BRDF model suitable for both physical analysis and path tracing. The model is based on the separation of spectral and geometrical aspect of the BRDF and allows a compact and efficient representation of isotropic, anisotropic and/or spectral BRDFs. After a brief survey of BRDF and wavelet theory, we present our software architecture for generic wavelet transform and how to use it to model BRDFs. Then, modelling results are presented on real and virtual BRDF measurements. Finally, we show how to exploit the multiresolution property of the wavelet encoding to reduce the variance by importance sampling in a path tracing algorithm. ACM CSS: I.3.7 Computer Graphics—Three‐Dimensional Graphics and Realism