SSS plastic and rubber

[neo0.]

I think rubber can be done by turning specularity to zero, but im not quite sure how to do this..

[aleksandera]

In Blendigo?

[Borgleader]

Would be nice if you'd provide the basic info we need to answer you Not all exporters are the same y'know.

[CTZn]

Hi neO, I understand your concern, hopefully the following will answer some of your recent questions:

Introduction
Walk into a darkened room and turn on the lights. The gleam of furniture, the color and texture of carpets and curtains are instantly visible. A simple everyday act-but the science behind it is a complex and increasingly important part of materials design and manufacturing. Light from a light source hits a surface and part of it is scattered and transmitted to your eyes. The visual perception of an object- what we call its appearance arises from the interaction of incident light with the object's surface geometry or texture. Optical properties of the material (such as index of refraction or polarization) also play a very important role. Will there come a day when given the microstructural and optical properties of a material a computer program can create the image of a chair made with that material? Making that day a reality will depend on the outcome of research done today.
For some years computer programs have produced images of scenes based on a simulation of scattering and reflection of light off one or more surfaces. In response to increasing demand for the use of rendering in design and manufacturing, the models used in these programs have undergone intense research in the computer graphics community. In particular, more physically realistic models are sought (i.e. models that more accurately depict the physics of light interaction). There has however been a lack of relevant optical measurements needed to complement the modelling. As part of the NIST competency project entitled "Measurement Science for Optical Reflectance and Scattering", Fern Hunt of MCSD 891 coordinated the development of a computer rendering system that utilizes high quality optical measurements that will be used to render physically realistic and potentially photorealistic images. We collaborated with Gary Meyer and Harold Westlund of Computer and Information Sciences Department at the University of Oregon. Michael Metzler of Isciences Inc. developed optical measurement protocols that could be used to generate appropriate data and models for the rendering program.
Goals and General Description of the NIST Appearance Project
As advances in material science and technology have enhanced the ability to manufacture coatings that are exciting and attractive in appearance, customer expectations for these products have increased and with this the challenge of characterizing and predicting appearance at the coatings formulation level. The ability to do this will require a much better understanding of the microstructural basis for coating appearance and the development of tools that can be used to firstly to identify important parameters in the formulation process that contribute to a desired appearance and secondly allow designers to visualize the surface appearance of a proposed formulation as part of a virtual formulation and manufacturing process. The ability to view a virtual end stage product will eventually pave the way to a computer graphics based standard for appearance. This is the vision guiding an ongoing project at the National Institute of Standards and Technology. The project was initiated in response to recommendations by industry , and the Council of Optical Radiation Measurements. The specific research goals are:
Develop advanced textural, spectral and reflectance metrologies and models for quantifying light scattering from a coating and its constituents and use the resulting measurements to generate scattering maps and validate physical models describing optical scattering from a coating and the relationship that the scattering maps have to the appearance of a coated object
Integrate measurements and models in making a virtual representation of the appearance of a coating system that can be used as a design tool capable of accurately predicting the appearance properties of coated objects from the optical properties of its constituents.
Simulate appearance of materials with specified ASTM gloss.
BRDF
One of the most general means to characterize the reflection properties of a surface is by use of the bi-directional reflection distribution function (BRDF), a function which defines the spectral and spatial reflection characteristic of a surface. The BRDF of a surface is the ratio of reflected radiance to incident irradiance at a particular wavelength.
where the subscripts i and r denote incident and reflected respectively, is the direction of light propagation, is the wavelength of light, L is radiance, and E is irradiance.NEFDS
The Nonconventional Exploitation Factors Data System (NEFDS) is a database of surface reflection parameters built upon a modified form of the Beard-Maxwell model, the NEF Beard-Maxwell (NEF-BM) model. NEFDS contains a suite of programs which use the database to perform complex radiometric calculations while taking into consideration the properties of the target material, the material background, the measuring sensors, and the atmospheric environment. The database contains pre-measured surface reflection parameters for over 400 materials corresponding to a wide variety of objects ranging from camouflage to white paint. Of key importance to this project, additional surface reflection parameters of other materials can be determined and used with NEFDS because a measurement protocol, using existing radiometric instruments, has been specified.
The large selection of pre-measured materials fall into 12 different categories: asphalt, brick, camouflage, composite, concrete, fabric, water, metal, paint, rubber, soil and wood. A NEFDS utility can be used to query for BRDF values of any of these materials at a range of wavelengths and any given geometry. The variation of material BRDFs available through NEFDS can be seen in Figure 2 below, which shows a visualization of the queried BRDFs of cement and lumber. Notice the significant difference in geometry that can be characterized by the modified Beard-Maxwell reflection model. This difference is also quite evident in the images rendered using the NEFDS protocol.
Oregon BRDF Library
The Oregon BRDF Library (OBL) is a compilation of a wide range of BRDF functions constructed over the previous 30 years in the fields of physics and computer graphics. OBL offers a uniform, object oriented interface in C++ to this collection of BRDF functions, and is flexible enough to incorporate future BRDF models.
The BRDFs included in OBL are:
Lambertian Diffuse Model
Minnaert Limb Darkening Model
Blinn Cloud & Dusty Surface Model
Cook-Torrance Specular Microfacet BRDF
Oren-Nayar Diffuse Microfacet BRDF
He-Torrance Comprehensive Analytic Model
Ward Anisotropic Model
Lafortune Generalized Cosine Lobe Model
Beard-Maxwell Bidirectional Reflectance Model
Phong Model
Poulin-Fournier Anisotropic Model
Arithmetic BRDF
iBRDF
Because of the uniform, object oriented C++ interface across all BRDFs, once a program is built for use with one BRDF model, substitution of any other is trivial. For this reason OBL has proven itself to be a useful and versatile tool. It has been used as the BRDF interface for a BRDF visualization system. A tool was created which determines the correspondence between the BRDF models of OBL and standard appearance scales. It has also been utilized extensively to provide the BRDF values for realistic image synthesis.
Virtual Light Meter
While it is important to be able to accurately depict the full BRDF of a material, there is also much merit in the ability to characterize a material with appearance attributes such as glossiness or haziness. To this end, people in the appearance industry have sought to develop and standardize a number of simple measurements and corresponding measuring devices which easily and objectively quantify the reflection properties of a surface. The result is a number of one-dimensional scales of appearance, such as gloss and haze, and inexpensive appearance measurement devices, such as glossmeters.
The standard specular gloss measurement defined by the American Society for Testing and Materials (ASTM) in ASTM method D523 measures the magnitude of light reflected in a small solid solid angle about the specular direction. ASTM method E430 specifies that haze is a measure of the fraction of light reflected in the off-specular direction to that reflected in the specular direction. These well defined measurements result in a single numerical value describing particular appearance attributes of the measured surface. An analogous measurement may be performed on the BRDF of a surface through computer simulation of the measurement protocol. In this way, a simulated glossmeter or hazemeter can be used to determine the gloss or haze of any arbitrary BRDF.
A computer program was developed which applies the measurement protocol of many of these standardized appearance tools (eg., glossmeters and hazemeters) to BRDFs. This new virtual light meter is essentially a customized integration tool, using numerical quadrature of the specified BRDF model over an adaptively subdivided source and receptor aperture (see Figure 5 below) to compute the final standard appearance value. In addition to being able to calculate the current standards, the virtual light meter can be customized for other measurements. The customizable parameters include the size and locations of the source and receptor apertures, the specular angle, the surface orientation, and the reflection model.
Figure 5: (left) Subdivision of light meter apertures using the 60 degree specular gloss specifications. The source and receptor apertures are oriented in directions and , 60 degrees down from the surface normal, N, in the plane of incidence. (right) Flux passing through receptor aperture due to one source aperture subdivision. Aperture sizes are not to scale.
Standard gloss and haze values are directly dependent upon the measured flux reflected off the surface and passing through the receptor aperture. The integration of this flux begins by subdividing the source aperture. For each sample point on the source, the receptor aperture is subdivided. Based on the initial results of the integration, the receptor aperture is adaptively subdivided until the discretely computed flux is within some specified tolerance. Figure 5 above shows an example of the flux due to one subdivided source element passing through the receptor. After this flux is determined, the next source sample point is chosen and the process is repeated. The source aperture continues to be subdivided until a specified tolerance is achieved. Figures 6 and 7 are two renderings of tiles with BRDF model parameters selected so as to achieve specific gloss and haze values.
Discussion of Rendering
In order to realize the overall purposes of the project, we set the goal of producing visually and radiometrically accurate renderings of selected aspects of the appearance of complex surfaces; seeking to provide a proven path from material measurement and characterization to object rendering. At this point, it would be helpful to answer the question, just what is rendering? It is the process of producing a synthetic image using a computer. To do this certain input parameters are required. If the scene is a conference room for example, a geometric description of the objects in the room; furniture, light sources, carpeting, windows, etc. must be provided. Secondly,there needs to be a description of the light sources, their location and radiometric properties. Thirdly the light scattering properties of an object in the room must be described-thus we must have the BRDF of the object (or BRTDF if light is also transmitted). Finally an observation viewpoint must be specified that defines the plane from which the image will be viewed. These parameters are used in an integral equation which describes the relationship between the amount of light incident to a surface in every direction and at a given point and the amount of light reflected or emitted from the surface in a given direction at that point:
where is the BRDF of a surface at the incident direction and reflected direction and is the wavelength of the incident light which we assume remains the same when reflected. , is the radiance of incoming light at (x, y, z) travelling in the direction given by . is similarly defined for light in an outgoing direction from (x, y, z). The units of radiance are where sr-steradians are solid angle units. The radiance of light incident at a point (x, y, z) can be related to the outgoing radiance at the same point so that the integral equation can be written as an equation for a single unknown radiance function L. A large part of the work done by the rendering program is the computation of a numerical solution of this equation, providing the radiance for each point in the scene that is visible to the observer or detector. A visual representation of the solution in a geometric description of the scene is the basis for the synthetic image. For more information on computer image synthesis see Glassner [GLASS95].
Radiance
This project utilized the Radiance Lighting Simulation and Rendering System (Radiance) to generate synthetic images. Radiance is a suite of programs built around an advanced distributed raytracer designed for realistic image synthesis. It was selected because it is a physically-based rendering system designed to accurately model the light behavior of a scene using physical units. Using such a system reinforces the validity of the results obtained by the physics based BRDFs within OBL and those generated from NEFDS. Additionally, the source code to Radiance is publicly available and the program is currently in wide use, aiding future work.
Radiance is a distributed ray tracer which utilizes Monte Carlo importance sampling to solve the rendering equation. The rendering equation specifies the reflected radiance in direction from the values of the surface's BRDF and the incident irradiance, integrated over all incident directions. As mentioned earlier the solution of this integral often is the most computationally expensive task of a rendering program. For this reason the solution to this integral is often found through the use of Monte Carlo integration.
Radiance was designed with built-in support of arbitrary BRDFs, but only for computing the direct contribution of the dominant light sources. (In Radiance, the dominant light sources are handled separately to reduce the variance introduced in the Monte Carlo evaluation of the rendering equation.). In order to correctly handle the contribution from the rest of the environment, a new shader, iBRDF, was developed which uses Monte Carlo importance sampling.
The BRDF models represented in the previous sections can capture subtle differences in surface light reflection. In order to generate synthetic images using BRDFs containing this level of generality requires a shader capable of capturing the detail which is available in the BRDFs. A new shader an extension of Radiance, called iBRDF, has been developed which accurately imitates this detail through its ability to utilize arbitrary BRDF functions.
We have developed an efficient method of performing this Monte Carlo integration. Instead of casting rays in a uniform distribution about the hemisphere and weighting the returned value by the reflectance, the ray distribution itself is weighted by the reflectance. This can be done in a straightforward manner when the BRDF is composed of invertible functions such as Gaussians. When the BRDF is represented discretely, either by taking measurements over the hemisphere or by sampling a non-invertible functional form, another method must be used to generate random variates for Monte Carlo integration. This can be accomplished by first subtracting the smallest hemisphere that fits within the BRDF data. This removes the diffuse or uniformly varying portion of the BRDF and leaves only the highly directional specular part. Walker's alias selection method [WALKE77] can be employed to create random variates from these remaining specular reflectances

The images in Figure 8 below display the improvement iBRDF offers in rendering surfaces modelled with arbitrary BRDFs. The top image which uses the built-in BRDF shader of Radiance, reflects the lights correctly, but there is no reflection at all of the indirect illumination from the surrounding checkered floor. Performing uniform sampling of the BRDF begins to captures this indirect contribution as seen in the middle figure, but the reflected image of the floor contains excessive noise. The best results are obtained with the importance sampling of iBRDF in the bottom figure. The reflection of the floor is accurately captured in the four spheres of this image using the same number of samples as the middle image.

Figure 8: Four spheres of increasing glossiness rendered using three different methods. (top) Radiance's built-in arbitrary BRDF shader incorrectly ignores the contribution from indirect illumination). (middle) Uniform Monte Carlo sampling (60 samples per pixel) results in an image filled with sampling noise. (bottom) Monte Carlo importance sampling with iBRDF (60 samples per pixel) correctly renders image.
The iBRDF enhancements to Radiance were used to render images of surfaces with widely varying material properties. The material properties of these surfaces were obtained through the use of various measurement methods as well as scattering simulations.

Rendered images of objects modelled with BRDFs obtained from NEFDS are detailed in NEFDS Images.
Images created using data measured by NIST are in NIST Images.
Other sources of data which were used to render images are available in Additional Images.
The measured data used in the NIST Images section and analysis of this data can be found in NIST Data Figures 9 through 12 are images of simulated objects coated with materials that appear in the NEFDS database. This was the first application of the enhanced rendering program iBRDF.
Figure 9: Shows three rendered vases rendered from opitical measurements taken from NEFDS. Materials are lumber, paint, and aluminum (left to right). Textures are added to simulate spatial variation. Figure 10: Five cubes. Top row use data from (left to right) cement and paint. Lower row: wood, aluminum and concrete. Textures are added to simulate spatial variation.
Figure 11: Shows rendered painted vases using three different models. Scene depicts vases in a room with nine lights distributed along a ceiling in a 3x3 grid. The third vase depicts retro reflecting rough surface.
Figure 12: Shows rendered vases in a room with nine lights as in Fig. 3. Materials modeled are concrete, paint, and camouflage (left to right).
iBRDF was used to render reflectance data coming from samples of black glass covered with a layer of clear epoxy. We were interested in seeing how well the rendered images showed the gloss variations in the samples. The reflectance modeling used for these samples makes use of surface topographical measurements - an approach that is new in the rendering field. Local surface height variations - measured using a scanning white light interferometer - were used to determine surface normals needed to compute the scattering direction. The BRDF was then computed by simulating the uniform illumination of the surface for various incident directions, and counting the number of scattered rays that reach detectors distributed over a hemisphere of directions. Details on sample preparation, reflectance modeling, and measurement can be found in McKnight et al. [MCKNI01]. Figure 13 shows two black glass tiles modelled with the generated BRDF. The tile on the left has rms roughness 201nm while the tile on the right is 805nm. The images were consistent with visual inspection of the tiles. The study demonstrated that gloss loss due to surface roughening could in fact be predicted by the rendering. This work is reported in Hunt et al. [HUNT01].
Figure 13: Rendering from reflectance data generated using the Ray method and a surface topographical map of coated epoxy samples with rms roughness values 201 nm (left) and 805 nm (right).
BRDF data obtained using the NEFDS measurement protocol - as discussed in NIST Data - was used to render images of surfaces composed of fine and coarse metallic flakes. The difference in the reflection properties of the two metallic surfaces can be seen in the rendered image of Figure 14. Figure 15 (left) contains a photograph of the same two surfaces placed in a light booth. To the right of the photograph is a rendering of the two surfaces using the modelled BRDF.
Figure 14: Coarse and fine metallic paint on vases
Figure 15: (left) Photograph of two samples dominated by fine and coarse metallic flake set in a light booth. (right) Rendered image of the same light booth and samples.
As discussed in the Light Meter section, standards exist for quantifying and measuring material appearance properties such as gloss and haze. Using these standards as a guide, we were able to render images of materials with specified appearance characteristics as shown in Figures 6 and 7.

A more recent appearance measurement, currently in the standardization process, is that used to characterize metallic paints and plastics. The method proposed for standardization specifies measurement of the tristimulus values at three angles. There is evidence that interpolation of three measured values can accurately characterize the appearance of metallic surfaces. A BRDF can then be generated from the interpolated tristimulus values and used with iBRDF to render synthetic images of objects modeled with metallic paint. Figure 16 below is an image of three vases modeled using tristimulus data measured from actual metallic paint samples and rendered using iBRDF. These vases demonstrate the sub-surface characteristic of metallic paint but not the usual glossiness attributed with metallic automobile finish. Combining the gonioapparent sub-surface reflection with a first-surface BRDF leads to a more realistic image as can be seen in Figure 17. The first-surface BRDF was chosen so that the 20 degree gloss values are 10 for the left shell and 60 for the right. More detail is provided in Westlund et al. [WESTL01].
Using the NEFDS measurement protocol, two surfaces painted with gray metallic paint were fit to the NEF-BM model. The paints were mixed so that one had mostly coarse metallic flakes while the other was dominated by fine metallic flakes. The paint with a greater number of fine flakes had a larger diffuse component due to more edge scattering. This is easily captured by the NEF-BM model and iBRDF as can be seen in Figure 18 below as well as the rendered images using this data. Figure 18 shows a good correspondence between the measured BRDF values and those obtained by evaluating the fit NEF-BM model. More detail is given in Nadal et al. [NADALxx]


Figure 18: In-plane near zero bistatic BRDF measurements of coarse and fine flake metallic sample.

The data from the fine and coarse metallic samples obtained by following the NEFDS measurement protocol is available in Excel format:
coarse - specular measurements
fine - specular measurements
coarse - shadowing measurement
fine - shadowing measurements
coarse - out of plane measurement
fine - out of plane measurements
coarse and fine - hemispherical measurements
In-plane BRDF measurements were performed on 30 G.U. (gloss units) and 70 G.U. gloss tiles and are available for download. These values were then compared to the BRDF values of two BRDF models with parameters selected by using the virtual light meter. Figure 19 is a plot of the BRDF values measured from the actual gloss tiles and the BRDF of a common analytical model versus reflected angle using light incident at 20 degrees.
Figure 19: BRDF values versus reflected angle for two gloss tiles with 20 degree gloss 30 G.U. and 70 G.U. The simulated gloss tiles using a common analytical BRDF models are also included. The model parameters were selected using the virtual gloss meter so as to achieve the same gloss values as the measured tiles.
Use of NIST Information
These World Wide Web pages are provided as a public service by the National Institute of Standards and Technology (NIST). With the exception of material marked as copyrighted, information presented on these pages is considered public information and may be distributed or copied. Use of appropriate byline/photo/image credits is requested.

Source: http://math.nist.gov/~FHunt/appearance/index.html

[CTZn]

To be sure to comply with disclaimer (last paragraph)

Project Personnel
Information Technology Laboratory

Fern Hunt
Gary Meyer (formerly University of Oregon, presently University of Minnesota)
Harold Westlund (formerly University of Oregon, presently Radical Entertainment)
Peter Walker (formerly University of Oregon)
Building and Fire Research Laboratory

Mary McKnight
Li Piin Sung
Physics Laboratory

Maria Nadal
Manufacturing Engineering Laboratory

Theodore Vorburger
Egon Marx

Indeed neO, there was a hidden message

[neo0.]

Would be nice if you'd provide the basic info we need to answer you Not all exporters are the same y'know.

Sorry, I meant in skindigo In skindigo

Rubber
http://news.thomasnet.com/images/large/459/459918.jpg

sss plastic
http://static.highend3d.com/tutorialima ... locker.jpg

[fused]

for the rubber i would try this:

a very dark grey(nearly black) phong with quite low IOR(1.2 maybe) and a very low exponent (like... 20?) and if its still too shiny blend it with an oren nayar with ~ same color (maybe a 60/40 blend).

[BbB]

I find exponent 50 works well for rubber. IOR can be higher than 1.2.

[fused]

i find that you should listen to what BbB says, he definitely knows what he is talking about

[neo0.]

The problem with using a specular mat for plastic is that plastic doesnt have the fresnel effect. The only "light property" that plastic has is opacity and sometimes sss.

Maybe a new mat type called diffuse transparent? I dont know..