Bidirectional Reflectance Distribution Function Model Research Articles

Microfacet rendering with diffraction compensation

AbstractThe traditional microfacet rendering models usually only consider the straight propagation of light and do not take into account the diffraction effect when calculating the radiance of outgoing light. However, ignoring the energy generated by diffraction can lead to darker rendering results when the object's surface has many small details. To address this issue, we introduce a diffraction energy term in the microfacet model to compensate for the energy loss caused by diffraction. Starting from the Fresnel‐Kirchhoff diffraction theorem, we combine it with the Cook‐Torrance model. By incorporating the computed diffraction radiance into the outgoing radiance of the microfacet, we obtain a diffraction‐compensated BRDF (Bidirectional Reflectance Distribution Function) model. Experimental results demonstrate that our proposed method has a significant effect in compensating for outgoing light and produces more realistic rendering results.

Utilising LANDHYPERNET data products over a deciduous broadleaf forest to validate Sentinel-2 and Landsat surface reflectance products

This paper demonstrates the use of a novel, autonomous hyperspectral surface reflectance data collected at Wytham Woods, United Kingdom (WWUK) as part of the LANDHYPERNET network for the validation of multispectral data from Sentinel-2, Landsat 8 and Landsat 9. The deployment of the HYPSTAR instrument at the site and the corresponding quality control of the data products is described. In addition, a methodology based upon metrological principles is outlined showing the propagation of uncertainties from the LANDHYPERNET and satellite data products to enable conformity testing of the satellite products using the satellite mission requirements. A total of 9 matchups are found for satellite validation at the site, where there is a cloud-free satellite scene and a corresponding LANDHYPERNET sequence, which has passed all quality checks, within two hours of the overpass. An analysis of the impact of the spatial variability of the site is presented and can account for up to 40% of the uncertainty associated with the in-situ surface reflectance data. There is no systematic bias in the Bottom-Of-Atmosphere reflectance data obtained from the LANDHYPERNET data in comparison to the satellite data. In the best case, differences of less than 2% are found for certain spectral bands. However, in the worst cases, relatively large differences are found which exceed 100%, this is affected by the relatively low reflectance values found in the visible bands. These differences could be caused by the spatial and temporal mismatch between the in-situ and satellite measurement, or due to shadowing caused by the flux tower. Further data quality control and assurance is needed to best choose data sets suitable for satellite validation. Incorporating a Bidirectional Reflectance Distribution Function model into the processing chain for the forest canopy is recommended. Overall, although there are areas to further characterise, the site provides a useful benchmark for which to develop techniques for validation of satellite surface reflectance products over a challenging environment.

Empirical BRDF model for goniochromatic materials and soft proofing with reflective inks.

The commonly used analytic bidirectional reflectance distribution functions (BRDFs) do not model goniochromatism, that is, angle-dependent material color. The material color is usually defined by a diffuse reflectance spectrum or RGB vector and a specular part based on a spectral complex index of refraction. Extension of the commonly used BRDFs based on wave theory can help model goniochromatism, but this comes at the cost of significant added model complexity. We measured the goniochromatism of structual color pigments used for additive color printing and found that we can fit the observed spectral angular dependence of the bidirectional reflectance using a simple modification of the standard microfacet BRDF model. All we need to describe the goniochromatism is an empirically-based spectral parameter, which we use in our model together with a specular reflectance spectrum instead of the spectral complex index of refraction. We demonstrate the ability of our model to fit the measured reflectance of red, green, and blue commercial structural color pigments. Our BRDF model enables straightforward implementation of a shader for interactive preview of 3D objects with printed spatially and angularly varying texture.

An improved BRDF hotspot model and its use in VLIDORT for studying the impact of atmospheric scattering on hotspot directional signatures in the atmosphere

Abstract. The term “hotspot” refers to the sharp increase in the reflectance occurring when incident (solar) and reflected (viewing) directions almost coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications. The RossThick–LiSparse–Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model is widely used in radiative transfer simulations, and the hotspot model mostly used is from Maignan–Bréon, but it typically requires large values of numerical quadrature and Fourier expansion terms in order to represent the hotspot accurately for its use coupled with atmospheric radiative transfer modeling (RTM). In this paper, we have developed a modified version based on the Maignan–Bréon's hotspot BRDF model that converges much faster numerically, making it more practical for use in the RTMs that require Fourier expansion of BRDF to simulate the top-of-atmosphere (TOA) hotspot signatures, such as in the RTM models using the Doubling–Adding or discrete ordinate method. Using the vector linearized discrete ordinate radiative transfer model (VLIDORT), we found that reasonable TOA–hotspot accuracy can be obtained with just 23 Fourier terms for clear atmosphere and 63 Fourier terms for atmosphere with aerosol scattering. In order to study the impact of molecular and aerosol scattering on hotspot signatures, we carried out a number of hotspot signature simulations with VLIDORT. We confirmed that (1) atmospheric molecule scattering and the existence of aerosol tend to smooth out the hotspot signature at the TOA and that (2) the hotspot signature at the TOA in the near-infrared is larger than in the visible, and its impact by surface reflectance is more significant. As the hotspot amplitude at the TOA with aerosol scattering included is smaller than that with molecular scattering only, the amplitude of hotspot signature at the surface is likely underestimated in the previous analysis based on the POLDER measurements, where the atmospheric correction was based on a single-scatter Rayleigh-only calculation. This modified model can calculate the amplitude of the hotspot accurately, and, as it agrees very well with the original RossThick model away from the hotspot region, this model can be simply used in conditions with and without hotspots. However, there are some differences in this modified model compared to the original Maignan–Bréon model for the scattering angles close to the hotspot point; thus, it may not be appropriate for those who need an exact representation of the hotspot angular signature.

Relationship between topographic parameters and BRDF for tungsten surfaces in the visible spectrum

In metallic fusion devices, parasitic light originating from multiple reflections on the wall is a major problem for the interpretation of optical diagnostics. Strong stray light affects several optical diagnostics in ITER. One possibility to cope with this reflected light is to use photonic simulation, which can accurately predict the behavior of light within complex 3D geometry. A prerequisite is to get a good description of the reflection model, represented by the Bidirectional Reflectance Distribution Function (BRDF), based on optical measurements of in-vessel materials. To avoid complicated measurements using goniophotometer to get the BRDF, one possibility is to link surface optical properties and topography characteristics, such as roughness measurements, for example, using the classical Bennett’s formula. Measurements were performed using two experimental goniophotometers to fully characterize the BRDF of tungsten samples with different roughness values. Surface topography was measured using a three-dimensional laser scanning confocal microscope. Several parameters were extracted from these measurements including the arithmetic average roughness (Ra), the root mean square roughness (RMS), the Surface Inclination Angle Distribution and furthermore its mean value δm and the power spectral density (PSD). The correlations of BRDF model parameters deduced from the measurements are compared with the previous topographic parameters. The initial results on several tungsten samples show that Ra, which is the usual measure of surface roughness, is not the most suitable metric to link with the reflection behavior of the surface. In contrast, the PSD and the surface inclination angle are interesting metrics for describing the reflected light.

MSL goniospectrophotometer measurement model

Measurements of both hemispherical reflectance and the bidirectional reflectance distribution function (BRDF) are of interest to a wide range of industries including computer graphics, remote sensing, lighting design, and cosmetics. The scale of directional reflectance is often realised at national metrology institutes using goniospectrophotometers, and by integrating BRDF measurements made over the hemisphere, the scale of hemispherical reflectance can also be realised. This paper describes the measurement model for BRDF and hemispherical reflectance using the MSL goniospectrophotometer, which uses rotation stages to adjust the angle of the sample. The measurement model is applied to measurements of a white Spectralon sample and a white ceramic tile to demonstrate the performance of the instrument. The relative standard uncertainty in the BRDF of a white Spectralon sample at 550 nm is less than 0.1% for in-plane measurements, while the relative standard uncertainty in the hemispherical reflectance of a white Spectralon sample at 560 nm is 0.27%.

Radiometric Improvement of Spectral Indices Using Multispectral Lightweight Sensors Onboard UAVs

Close-range remote sensing techniques employing multispectral sensors on unoccupied aerial vehicles (UAVs) offer both advantages and drawbacks in comparison to traditional remote sensing using satellite-mounted sensors. Close-range remote sensing techniques have been increasingly used in the field of precision agriculture. Planning the flight, including optimal flight altitudes, can enhance both geometric and temporal resolution, facilitating on-demand flights and the selection of the most suitable time of day for various applications. However, the main drawbacks stem from the lower quality of the sensors being used compared to satellites. Close-range sensors can capture spectral responses of plants from multiple viewpoints, mitigating satellite remote sensing challenges, such as atmospheric interference, while intensifying issues such as bidirectional reflectance distribution function (BRDF) effects due to diverse observation angles and morphological variances associated with flight altitude. This paper introduces a methodology for achieving high-quality vegetation indices under varied observation conditions, enhancing reflectance by selectively utilizing well-geometry vegetation pixels, while considering factors such as hotspot, occultation, and BRDF effects. A non-parametric ANOVA analysis demonstrates significant statistical differences between the proposed methodology and the commercial photogrammetric software AgiSoft Metashape, in a case study of a vineyard in Fuente-Alamo (Albacete, Spain). The BRDF model is expected to substantially improve vegetation index calculations in comparison to the methodologies used in satellite remote sensing and those used in close-range remote sensing.

Modeling the hotspot effect for vegetation canopies based on path length distribution

The hotspot effect, which refers to the increased reflectance in the solar direction, relies on the canopy structure. Numerous bi-directional reflectance distribution function (BRDF) models have been developed with careful attention to the hotspot effect. However, different assumptions may lead to very different shapes of BRDF, especially in the hotspot direction. With the development of high-resolution remote sensing satellites and three-dimensional (3D) mapping technologies, abundant 3D structural vegetation canopy information is available. However, traditional analytical models might not fully use these kinds of information. As a result, most of them are still very limited by their assumptions, such as the shape and spatial distribution of crowns. One BRDF model may do well only for one vegetation type and shows significant errors under other scenarios. We propose a new model named PATH_RT, which can model the hotspot effect at both the leaf and crown levels using the path length distribution (PLD). PLD can be easily obtained with the 3D information of the canopy at given remote sensing geometries. We validated the bi-directional reflectance factor (BRF) predicted by the PATH_RT model with the simulated scenes and field measurements which were produced by a 3D radiative transfer model LESS and UAV observations respectively. Compared to traditional analytical models SAILH, 4SAIL2, GORT, and 5SCALE, the PATH_RT model exhibits the highest accuracy, reducing the average RMSE by 86%, 66%, 52%, and 58% for the hotspot region respectively. PATH_RT accurately depicts the hotspot effects at the canopy and leaf levels and is highly efficient (99%, 75%, 86%, 99%, and 99% less time spent for one repeat run compared to LESS, SAILH, 4SAIL2, GORT, and 5SCALE models, respectively). Considering its high accuracy and efficiency, PATH_RT is expected to improve the accuracy and efficiency of remote sensing inversion.

Retrieving land surface reflectance anisotropy with Sentinel-3 observations and prior BRDF model constraints

Sentinel-3 supports governmental policies and European programmes such as the Copernicus Global Land Service with daily monitoring of the land surface by measuring the Earth's surface with its multi-band wide-swath visible and near-infrared radiometers. To fully exploit Sentinel-3 data sets, performing a correction of directional effects caused by the land surface reflectance anisotropy properties is mandatory. This correction requires first an estimation of the bidirectional reflectance distribution function (BRDF) to be properly achieved. The quality and robustness of global estimates of key land surface variables derived from space-borne sensors depend clearly on an accurate assessment of the spectral BRDF.Here, we present an algorithm aiming at harnessing the Sentinel-3's wide imaging swath to retrieve the land surface BRDF, approximating it via a kernel-driven semi-empirical model which can then be used to normalise the observations to a common Sun-sensor geometry. Our algorithm, named ReBeLS (Regularised BRDF inversion for Land Surface), uses a temporally regularised BRDF inversion approach and, together with prior knowledge of the land surface BRDF obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, assimilates Sentinel-3 surface reflectance observations. We focus on the results derived from observations provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensors; however, the algorithm can also be applied to reflectance observations acquired by the Sea and Land Surface Temperature Radiometer (SLSTR). The retrieved BRDF model parameters reproduce within uncertainties the directional signature as seen by the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor. The generated Sentinel-3 normalised surface reflectance products are a significant advance compared with time series of directional surface reflectance and maximum value composite images, showing a statistically significant (p≪0.05) correlation (r>0.9) with VIIRS nadir BRDF-adjusted surface reflectance. Furthermore, the ReBeLS Sentinel-3 BRDF products offer enough flexibility to allow users to compute spectral surface albedos or to normalise surface reflectance to any Sun-sensor configuration.

Analyzing GOES-R ABI BRDF-adjusted EVI2 time series by comparing with VIIRS observations over the CONUS

The Advanced Baseline Imager (ABI) instruments onboard the Geostationary Operational Environmental Satellite-R series (GOES-R) provide new opportunities to expand their applications in global terrestrial ecology and environmental sciences. It offers a 5–15 min temporal resolution and 0.5–1 km spatial resolution that greatly benefit those environmental applications where spatial resolution can be compromised in favor of more frequent imagery. However, ABI captures reflected radiation at varying sun positions throughout the day, causing significant diurnal variations. The varying off-nadir view across ABI full disk presents further challenges for generating comparable and consistent vegetation observations, such as the two-band Enhanced Vegetation Index (EVI2). To address these challenges, three Bidirectional Reflectance Distribution Function (BRDF) models (RossThick-LiSparse-Reciprocal (RTLSR), Walthall, and Tian models) are examined to generate high-quality BRDF-adjusted EVI2 based on GeoNEX GOES-16 ABI surface reflectance. The ABI BRDF retrievals are then systematically compared to the corresponding VIIRS (Visible Infrared Imaging Radiometer Suite) Nadir BRDF-Adjusted Reflectance (NBAR) products (VNP43 V001) retrieved from RTLSR model. The viewing geometry effects are further analyzed over various land cover types using time series analysis, Pearson Correlation Coefficients (PCC), and Relative Difference (RD) between ABI and VIIRS high-quality time series inversions. The results indicate that the RTLSR model fits well the diurnal ABI observed reflectance and EVI2 values (even for the hot-spot phenomena), and outperforms the Walthall and Tian models for generating ABI high-quality BRDF retrievals with consistently low Root Mean Square Error (RMSE<0.02) and Weight of Determination (WoD < 0.5) across the CONUS. The annual proportion of ABI high-quality BRDF retrievals with a 3-day moving window using the RTLSR model is spatially and statistically comparable to that of VIIRS NBAR with a 16-day retrieval period. Compared to the time series of high-quality VIIRS NBAR EVI2, the BRDF adjusted ABI EVI2 time series still show considerable impacts of view zenith angles over evergreen forest, shrubland, and savanna pixels. However, such impacts are not significant over spatially homogeneous pixels such as cropland and deciduous forests. These results emphasize the need for BRDF correction when considering the impact of off-nadir observations from geostationary satellites on vegetation monitoring. They also demonstrate the advantages of geostationary satellites in capturing rapid changes in greenness trajectories.

The influence of BRDF effects and representativeness of training data on tree species classification using multi-flightline airborne hyperspectral imagery

Airborne hyperspectral imagery provides detailed spatial and spectral information that can be used for tree species classification, but its application in practical forest inventories over large areas is limited. This study investigates the impact of (i) bidirectional reflectance distribution function (BRDF) effects on multi-flightline airborne imagery and (ii) the selection strategy for training data on tree species classification accuracy over large forested areas. Hyperspectral imagery was acquired using the Chinese Academy of Forestry’s LiDAR, CCD, and Hyperspectral system (CAF-LiCHy) over the Mengjiagang Forest Farm (MFF, ∼155 km2) in northeastern China, which includes common keystone coniferous species in northern China’s temperate-boreal ecozones. A kernel-driven BRDF model based on mNDVI704.50 stratification was applied to airborne hyperspectral data acquired from multi-flightlines. We developed three training data selection strategies, namely #1 sampling over the entire area, #2 localized sampling over the area for each flight date, and #3 gradually decreasing sampling in size. Additionally, two validation assessment strategies were respectively employed: independent data over the entire area and independent data in areas with inconsistent classification labeling between using BRDF corrected and uncorrected hyperspectral imagery. These strategies were implemented to perform a comprehensive cross-comparison of tree species classification accuracy using BRDF corrected and uncorrected airborne hyperspectral imagery. The study found that the representativeness of the training data (in terms of solar-viewing geometry distribution and size) has a stronger impact on the classification accuracy of tree species than the BRDF effects in airborne hyperspectral imagery. By utilizing BRDF corrected hyperspectral imagery with a majority of representative training data, the mean overall accuracy of tree species classification improves by ≤ 2.9 % compared to uncorrected imagery. Notably, BRDF correction has improved classification accuracy by 11.5 % in inconsistently labeled areas, mainly concentrated in the spatial overlap regions of adjacent flightlines. These findings provide valuable insights for improving tree species classification accuracy in large forested areas using multi-flightline airborne hyperspectral imagery.

BRDF modeling and optimization of a target surface based on the gradient descent algorithm

Addressing the current challenges in modeling and optimizing the bidirectional reflectance distribution function (BRDF) for the target surface, an improved six-parameter semi-empirical model is proposed based on an existing five-parameter semi-empirical model. In comparison with the original five-parameter model, the new, to the best of our knowledge, model considers reciprocity, and the results demonstrate that as the incident angle increases, the fitting accuracy of the six parameters gradually surpasses that of the five parameters. Additionally, this paper employs a machine learning optimization algorithm, namely, the gradient descent method, for optimizing the BRDF. The algorithm was comprehensively compared with other optimization methods, revealing that for the same dataset, the gradient descent method exhibited the smallest fitting errors. Subsequently, utilizing this algorithm for fitting experimental data resulted in errors consistently within 3%, confirming the reliability and accuracy of this optimization algorithm.

An overview of BRDF models in computer graphics

This paper aims to provide an overview of the Bidirectional Reflectance Distribution Function (BRDF) and the various parametric numerical models, known as BRDF models, used to make it easier and more efficient to use BRDF data in computer graphics. It introduces the basics of BRDF and classifies and describes some well-known BRDF models, including their advantages and disadvantages. The paper also covers the concept of data-driven models, which can achieve a high degree of realism by directly acquiring and using measured BRDF data in the rendering process.

Enhancing Leaf Area Index Estimation with MODIS BRDF Data by Optimizing Directional Observations and Integrating PROSAIL and Ross–Li Models

The Leaf Area Index (LAI) is a crucial vegetation parameter for climate and ecological models. Reflectance anisotropy contains valuable supplementary information for the retrieval of properties of an observed target surface. Previous studies have utilized multi-angular reflectance data and physically based Bidirectional Reflectance Distribution Function (BRDF) models with detailed vegetation structure descriptions for LAI estimation. However, the optimal selection of viewing angles for improved inversion results has received limited attention. By optimizing directional observations and integrating the PROSAIL and Ross–Li models, this study aims to enhance LAI estimation from MODIS BRDF data. A dataset of 20,000 vegetation parameter combinations was utilized to identify the directions in which the PROSAIL model exhibits higher sensitivity to LAI changes and better consistency with the Ross–Li BRDF models. The results reveal significant variations in the sensitivity of the PROSAIL model to LAI changes and its consistency with the Ross–Li model over the viewing hemisphere. In the red band, directions with high sensitivity to LAI changes and strong model consistency are mainly found at smaller solar and viewing zenith angles. In the near-infrared band, these directions are distributed at positions with larger solar and viewing zenith angles. Validation using field measurements and LAI maps demonstrates that the proposed method achieves comparable accuracy to an algorithm utilizing 397 viewing angles by utilizing reflectance data from only 30 directions. Moreover, there is a significant improvement in computational efficiency. The accuracy of LAI estimation obtained from simulated multi-angle data is relatively high for LAI values below 3.5 when compared with the MODIS LAI product from two tiles. Additionally, there is also a slight improvement in the results when the LAI exceeds 4.5. Overall, our results highlight the potential of utilizing multi-angular reflectance in specific directions for vegetation parameter inversion, showcasing the promise of this method for large-scale LAI estimation.

Modeling and analysis of spectral polarization BRDF based on Microfacet theory

The polarization bidirectional reflectance distribution function (p-BRDF) model derived from the microfacet theory is utilized for ascertaining wavelength dependent terms in the model. The spectral p-BRDF model of a given object surface is established according to the spectral models of the complex refractive index and the width parameter. Each parameter in the spectral p-BRDF model has a clear physical meaning, making it more physical than Cook-Torrance BRDF model. The particle swarm optimization algorithm combined with in-plane BRDF experimental data is used for analyzing the spectral p-BRDF of a brass surface in the visible light band. The results show that the BRDF calculated by the model is in good agreement with the experimental data, which is validated by the adjusted R2, the optimal value is 0.9872. The peak value of BRDF is found to increase with increase in wavelength for different polarization states. There exists a certain incident angle at which the BRDF initially increases and then decreases as the reflection angle is increased. The BRDF peak appears in the direction of specular reflection, and all the BRDF peaks increase with the increase in the incident angle. The polarization state, wavelength, surface roughness, and permittivity are found to be the important factors affecting the distribution of BRDF on the object surface. A combination of the spectral p-BRDF model and limited experimental data related to the object surface under different testing conditions can be used to predict the spatial reflection characteristics of the surface of a given object.

Modeling reflection by structured building-integrated photovoltaics

Evaluating the reflection of solar radiation by Building Integrated Photovoltaics (BIPV) with structured front-glass is challenging for two reasons. First, the resulting irregular scattering of light cannot be accounted for by simple reflection models. Second, the locations where the evaluation needs to take place cannot be predicted by simple geometric considerations, as in the case of mirror-like reflection. The detailed modelling of a BIPV module featuring a periodical, linear structure based on the scanning of its surface is described. The application of the model is demonstrated in the assessment of solar reflection by a concave array of the modules. The stochastic sampling of the BIPV modules from each location in a dense sensor grid, that is required to ensure that no local concentration of reflected solar radiation is missed, is supported by re-orienting the sensors. Rather than relying on a data-driven Bidirectional Reflectance Distribution Function (BRDF) model of limited directional resolution, the geometric structure of the front-glass is modelled by modulating the normal of a mirror-like surface. The method allows an accurate and dense simulation with moderate computational demand.

The Method of Multi-Angle Remote Sensing Observation Based on Unmanned Aerial Vehicles and the Validation of BRDF

The measurement of bidirectional reflectivity for ground-based objects is a highly intricate task, with significant limitations in the capabilities of both ground-based and satellite-based observations from multiple viewpoints. In recent years, unmanned aerial vehicles (UAVs) have emerged as a novel remote sensing method, offering convenience and cost-effectiveness while enabling multi-view observations. This study devised a polygonal flight path along the hemisphere to achieve bidirectional reflectance distribution function (BRDF) measurements for large zenith angles and all azimuth angles. By employing photogrammetry’s principle of aerial triangulation, accurate observation angles were restored, and the geometric structure of “sun-object-view” was constructed. Furthermore, three BRDF models (M_Walthall, RPV, RTLSR) were compared and evaluated at the UAV scale in terms of fitting quality, shape structure, and reflectance errors to assess their inversion performance. The results demonstrated that the RPV model exhibited superior inversion performance followed, by M_Walthall; however, RTLST performed comparatively poorly. Notably, the M_Walthall model excelled in capturing smooth terrain object characteristics while RPV proved applicable to various types of rough terrain objects with multi-scale applicability for both UAVs and satellites. These methods and findings are crucial for an extensive exploration into the bidirectional reflectivity properties of ground-based objects, and provide an essential technical procedure for studying various ground-based objects’ in-plane reflection properties.

Modified Model of Polarized Bidirectional Reflectance Distribution Function Used for Light Detection and Ranging (LiDAR)

In order to analyze the performance of a light detection and ranging system based on polarization modulation, it is necessary to theoretically analyze and model the polarization scattering characteristics of common target materials. In this paper, the shortcomings of the classical Hyde pBRDF (polarization bidirectional reflectance distribution function) model are analyzed. Based on the research results of many researchers in recent years, a new six-parameter pBRDF model is proposed. To verify the accuracy of the proposed model, this paper builds a measurement system for the polarization scattering characteristics of the target surface in the laser active imaging scene, and the polarization scattering characteristics of two common materials, namely a white paint coating and an aluminum plate, are measured. Based on the measurement results of the DOP (degree of polarization) of the scattered light of the target material and the BBO-FA (biogeography-based optimization-Firefly algorithm) algorithm, we performed inversion calculations on the key parameters of the target material. Using the parameters of the target material obtained via inversion, we use the model to simulate the Stokes vectors of the target and compare the simulated values of Stokes vectors with the measured values to verify the accuracy of the model. The verification results show that the simulation results of Stokes vectors are in good agreement with the measurement results for these two materials, and the introduction of various improvements to the model can effectively improve the accuracy of the model, which provides a tool for studying the performance parameters of a laser three-dimensional imaging system based on polarization modulation.

Generating Parametric BRDFs from Natural Language Descriptions

AbstractArtistic authoring of 3D environments is a laborious enterprise that also requires skilled content creators. There have been impressive improvements in using machine learning to address different aspects of generating 3D content, such as generating meshes, arranging geometry, synthesizing textures, etc. In this paper we develop a model to generate Bidirectional Reflectance Distribution Functions (BRDFs) from descriptive textual prompts. BRDFs are four dimensional probability distributions that characterize the interaction of light with surface materials. They are either represented parametrically, or by tabulating the probability density associated with every pair of incident and outgoing angles. The former lends itself to artistic editing while the latter is used when measuring the appearance of real materials. Numerous works have focused on hypothesizing BRDF models from images of materials. We learn a mapping from textual descriptions of materials to parametric BRDFs. Our model is first trained using a semi‐supervised approach before being tuned via an unsupervised scheme. Although our model is general, in this paper we specifically generate parameters for MDL materials, conditioned on natural language descriptions, within NVIDIA's Omniverse platform. This enables use cases such as real‐time text prompts to change materials of objects in 3D environments such as “dull plastic” or “shiny iron”. Since the output of our model is a parametric BRDF, rather than an image of the material, it may be used to render materials using any shape under arbitrarily specified viewing and lighting conditions.

Analysis of scratch visibility on polymeric surfaces using 3D roughness measurement and the bidirectional reflectance distribution function (BRDF)

The evolution of visual aspect of polymers with wear is a fundamental problem in numerous industries. In this article, we study the visibility of scratches. Fifty parallel micro-scratches are performed on three samples differing only by their values of gloss. Scratches are much more visible on glossy samples than on mat ones, in agreement with previous works. To interpret these results, a bidirectional reflectance distribution function (BRDF) model is developed to compute the way a surface scatters light based on a roughness measurement described by a distribution of microfacet orientations. In this paper, this BRDF model is applied to investigate the influence of the scratches on the light scattering properties of a surface. Roughness measurements are performed on the surfaces before and after scratching. Then, the BRDFs are computed and compared to each other. The visibility of scratches strongly depends on how much they modify the roughness slope distribution function of the surface. Depending on the initial gloss of the surface, this leads to more or less contrast. Finally, a visibility criterion is proposed based on this contrast effect.