Phong Model Research Articles

Recognition of ecological vegetation fairy circles in intertidal salt marshes from UAV LiDAR point clouds

• Fairy circles are detected from 3D point clouds of UAV LiDAR for the first time. • A new model is proposed to correct the intensity data of UAV LiDAR. • Merely corrected intensity data can reliably be used for point cloud filtering. • Absent instantaneous UAV LiDAR scanning distances and scan angles are recovered. • The proposed method is promising for spatial patterns recognition in other scenes. Fairy circles (FC) are a type of spatial self-organized patterns that widely exist in various vegetation ecosystems and the accurate detection and quantitative characterization of these mysterious circles remain a technical challenge. In this study, vegetation FC in intertidal salt marshes are recognized from the derived reflectance information (backscattered intensity) and geometric quantities of light detection and ranging (LiDAR) carried on unmanned aerial vehicle (UAV). The specular effect on the UAV LiDAR intensity data over nadir regions of wet salt marshes is eliminated using the laser radar equation and Phong model where the absent distances and incidence angles are approximately retrieved on the basis of geometric and temporal relations in data collection. The FC are progressively recovered through three interconnected procedures. First, the retrieved reflectance information is used to discriminate the mudflat and vegetation points. Second, a spatial connectivity clustering algorithm is utilized on the extracted vegetation points to form individual spatially disconnected clusters. Finally, FC and regular vegetation are successfully recognized by jointly using the salient, size, and circularity features of the generated clusters. A multi-echo UAV LiDAR system is used for data collection at an intertidal salt marsh to assess the feasibility and prospects of the proposed method. Taking the manual detection results from the orthophoto generated by images of a UAV camera system as a reference, the missing detection rate, false detection rate, and area detection error of the proposed method are 6%, 9%, and 5%, respectively. Results suggest that UAV LiDAR is an extremely promising technique to characterize the geometric properties (e.g., location, size, and quantity) of FC from a holistic perspective.

Resident space object (RSO) attitude and optical property estimation from space-based light curves

With the increase in the number of objects orbiting Earth, Space Situational Awareness (SSA) has becoming an important area of research in the space sector. Currently most sensors that contribute to SSA are large dedicated optical or radar stations, such as space fence (Pechkis, et al., 2015). With the increase in low resolution sensors in LEO there is a growing potential to utilize these to augment current SSA efforts. Star trackers are readily available and used in space for attitude determination, with recent work performed to demonstrate the benefit of using spaced-based optical measurements for Resident Space Object (RSO) detection. In this paper, we describe the interpretation of space-based measurement for light curve of an RSOs to estimate the RSOs shape, attitude and optical properties. In this model, two Bidirectional Reflectance Distribution Functions (BDRF’s) are compared, namely a defined facet model and an anthropic Phong model. From the initial results an RSOs shape, attitude, optical properties can be estimated with basic a-priory information on the shape of the RSO with both models.

Multispectral LiDAR point cloud highlight removal based on color information.

With the rapid development of light detection and ranging (LiDAR) technology, multispectral LiDAR (MSL) can realize three-dimensional (3D) imaging of the ground object by acquiring rich spectral information. Although color restoration has been achieved on the basis of the full-waveform data of MSL, further improvement of the visual effect of color point clouds still faces many challenges. In this paper, a highlight removal method for MSL color point clouds is proposed to explore the potential of 3D visualization. First, the MSL reflection model are introduced according to radar equation and Phong model, and the restored color of the MSL point clouds is determined to comprise diffuse and specular components. Second, a data conversion method is proposed to improve the massive point cloud processing efficiency by spatial dimension reduction and data compression. Then, the visual saliency map after color denoising is used to obtain the highlight region, the unknown information of which is recovered based on the global or local color information. Finally, three representative targets are selected and evaluated by qualitative and quantitative validation, which verifies that the method can effectively recover the high-quality highlight-free point clouds of MSL.

Measurement and modeling of reflection characteristics of hole inner surface based on endoscopic image

Reflection characteristic is the law of specular and diffuse reflection after receives light of the surface, usually expressed by the ratio of irradiance of incident direction to the emissivity of observation direction, namely bidirectional reflection distribution function (BRDF). A method to measure BRDF for hole inner surface based on the endoscope image and morphology is proposed, which established the relationship between BRDF and morphology. Taking stainless steel and galvanized internal threaded as an example, the axial images in the holes are collected, and the BRDF of internal surface is calculated through the image brightness and the normal direction. The distribution characteristics of BRDF on the inner surface of different materials are analyzed. Based on the Phong model, the inBRDF parameter model of the inner surface is established. Research shows that the inBRDF model is consistent with measured results, and the average fitting error of the two materials is only 6.22%. The inBRDF method of hole inner surface proposed in this paper has high measurement efficiency and strong universality of equipment, and it is preferred to be applied on hole inner surface quality detection accordingly.

A research on the spectral Bidirectional Reflectance Distribution Function measurement of tensile polyimide film rough surfaces

The reflection distribution function is important for calculating the scatter distribution on material surface. In order to calculate the light scatter distribution of the tensile polyimide, it is necessary to investigate the relationship between the Bidirectional Reflectance Distribution Function (BRDF) of tensile polyimide film with its surface roughness and the incident light, and test the fitting function to evaluate the BRDF. The measurement spectrum is from 300nm to 750nm. The measurement results show that the BRDF of polyimide film are different with the tensile stress, and the reflectance intensity in the reflective direction decreases with the increase of the tensile stress, increases with the wavelength of the incident light. The reflectance intensity is associated with the roughness changing of polyimide surfaces for the tensile stress, as the roughness increases with the more stress, and the intensity increases in the none reflective direction. The fitting precision is up to 95% applying with the T-S model compared with the classical Phong model. The research data can be used to evaluate the reflective intensity of pharmaceutical packaging film, medical light barrier film, thermal insulation film and other film materials.

The Open-Source Framework for 3D Synthetic Aperture Radar Simulation

Synthetic Aperture Radar (SAR) simulation is widely used for system design, processing techniques development, and mission planning. However, there is no readily available and free framework for SAR raw signal simulation, and many teams and organizations struggle with developing their own simulator from scratch, repeating work that others have done earlier. This work’s purpose is to create a simple, open-source SAR simulation framework that can be used for many purposes and is available for free to everyone. It is a ready tool for SAR processing techniques verification, and thanks to its open-source nature, it fosters collaboration between scientists, both on the simulation and the tool development. Moreover, as the simulation’s underlying mathematics is described in detail in this article, it may serve as a handbook for the radar’s simulation. The simulator was prepared as a stand-alone, multiplatform software working in the Matlab/Octave environment. It is available in an online software repository that allows others to contribute to the original code or to create forks of it. The simulator supports monostatic, bistatic, and multistatic configurations with shadowing. The surface model includes roughness using the modified Phong model, transparency, and complex reflectivity. The antenna pattern is defined with a general two-dimensional model. The article is concluded with the simulator’s demonstrations, including antenna pattern, Doppler shift, and various surface parameters. It is expected that this simulator will be widely adopted and improved by many collaborators.

基于改进Phong模型的天基光学观测GEO目标姿态估计方法

基于改进Phong模型的天基光学观测GEO目标姿态估计方法

A Novel Approach to Droplet's 3D Shape Recovery Based on Mask R-CNN and Improved Lambert⁻Phong Model.

Aiming at the demand for extracting the three-dimensional shapes of droplets in microelectronic packaging, life science, and some related fields, as well as the problems of complex calculation and slow running speed of conventional shape from shading (SFS) illumination reflection models, this paper proposes a Lambert–Phong hybrid model algorithm to recover the 3D shapes of micro-droplets based on the mask regions with convolutional neural network features (R-CNN) method to extract the highlight region of the droplet surface. This method fully integrates the advantages of the Lambertian model’s fast running speed and the Phong model’s high accuracy for reconstruction of the highlight region. First, the Mask R-CNN network is used to realize the segmentation of the highlight region of the droplet and obtain its coordinate information. Then, different reflection models are constructed for the different reflection regions of the droplet, and the Taylor expansion and Newton iteration method are used for the reflection model to get the final height of all positions. Finally, a three-dimensional reconstruction experimental platform is built to analyze the accuracy and speed of the algorithm on the synthesized hemisphere image and the actual droplet image. The experimental results show that the proposed algorithm based on mask R-CNN had better precision and shorter running time. Hence, this paper provides a new approach for real-time measurement of 3D droplet shape in the dispensing state.

Backscattering properties of topographic targets in the visible, shortwave infrared, and mid-infrared spectral ranges for hard-target lidars.

Hard-target lidars rely on the reflectivity and backscattering properties of topographic targets, which are rather difficult to evaluate, resulting in uncertainties when assessing the performance of the instrument. In this work, backscattering properties and hemispherical reflectance of topographic targets are measured in the visible, near-infrared, and mid-infrared spectral ranges. A laboratory setup mimicking a hard-target lidar is used to measure the backscattered signals at various angles of incidence, which are then fitted using a bidirectional reflectance distribution function Phong model. We show that these results are useful for optimizing active stand-off detection and hard-target lidars and for increasing their overall efficiency.

Error analysis and correction for color in laser triangulation measurement

With the aim to improve the accuracy of laser triangulation measurements, this paper proposes to analyze the error caused by different colors of a measurand surface and proposes a compensation method. By investigating the fundamentals of laser triangulation, the color error is obtained via the Phong model, while the relationship between the measurement error and color is obtained through experiments. Based on the above results, a color error compensation method is proposed, which produces a color-error library according to the error of the different colors. Thus, the correlation between the error and color of the measurand surface is also calculated. The experimental data demonstrates that this method can reduce the error to 10% and maintain the color measurement error to within 90 μm. Moreover, this technique is of great significance to measure a measurand surface with different colors.

Specular Reflection Effects Elimination in Terrestrial Laser Scanning Intensity Data Using Phong Model

The intensity value recorded by terrestrial laser scanning (TLS) systems is significantly influenced by the incidence angle. The incidence angle effect is an object property, which is mainly related to target scattering properties, surface structures, and even some instrumental effects. Most existing models focus on diffuse reflections of rough surfaces and ignore specular reflections, despite that both reflections simultaneously exist in all natural surfaces. Due to the coincidence of the emitter and receiver in TLS, specular reflections can be ignored at large incidence angles. On the contrary, at small incidence angles, TLS detectors can receive a portion of specular reflections. The received specular reflections can trigger highlight phenomenon (hot-spot effects) in the intensity data of the scanned targets, particularly those with a relatively smooth or highly-reflective surface. In this study, a new method that takes diffuse and specular reflections, as well as the instrumental effects into consideration, is proposed to eliminate the specular reflection effects in TLS intensity data. Diffuse reflections and instrumental effects are modeled by a polynomial based on Lambertian reference targets, whereas specular reflections are modeled by the Phong model. The proposed method is tested and validated on different targets scanned by the Faro Focus3D 120 terrestrial scanner. Results imply that the coefficient of variation of the intensity data from a homogeneous surface is reduced by approximately 38% when specular reflections are considered. Compared with existing methods, the proposed method exhibits good feasibility and high accuracy in eliminating the specular reflection effects for intensity image interpretation and 3D point cloud representation by intensity.

3D laser imaging using new photometric Gaussian range equation and Phong model reflection

This article tackles the challenging problem in creating 3D photorealistic scenes from mobile laser imaging data. The major disadvantage of this method is both its range and quality of reconstructed images. These parameters are strongly dependent among other on target materiel, size of the laser footprint and atmospheric attenuation. However, in this paper, we propose a new reformulation of the photometric Gaussian beam range equation on the basis of the Phong model reflection. The substantial contribution of this model in improving the quality of back return signals diffused from targets is confirmed. We prove that the novel model brings a capital gain in terms of the quality and maximum range of detection. This will increase significantly the resolution of reconstructed images and the accuracy of range measurement. Thus, the proposed approach appears promising for 3D active sensing for mapping and scenes modeling.

Efficient 3D morphable face model fitting

3D face reconstruction of shape and skin texture from a single 2D image can be performed using a 3D Morphable Model (3DMM) in an analysis-by-synthesis approach. However, performing this reconstruction (fitting) efficiently and accurately in a general imaging scenario is a challenge. Such a scenario would involve a perspective camera to describe the geometric projection from 3D to 2D, and the Phong model to characterise illumination. Under these imaging assumptions the reconstruction problem is nonlinear and, consequently, computationally very demanding. In this work, we present an efficient stepwise 3DMM-to-2D image-fitting procedure, which sequentially optimises the pose, shape, light direction, light strength and skin texture parameters in separate steps. By linearising each step of the fitting process we derive closed-form solutions for the recovery of the respective parameters, leading to efficient fitting. The proposed optimisation process involves all the pixels of the input image, rather than randomly selected subsets, which enhances the accuracy of the fitting. It is referred to as Efficient Stepwise Optimisation (ESO).The proposed fitting strategy is evaluated using reconstruction error as a performance measure. In addition, we demonstrate its merits in the context of a 3D-assisted 2D face recognition system which detects landmarks automatically and extracts both holistic and local features using a 3DMM. This contrasts with most other methods which only report results that use manual face landmarking to initialise the fitting. Our method is tested on the public CMU-PIE and Multi-PIE face databases, as well as one internal database. The experimental results show that the face reconstruction using ESO is significantly faster, and its accuracy is at least as good as that achieved by the existing 3DMM fitting algorithms. A face recognition system integrating ESO to provide a pose and illumination invariant solution compares favourably with other state-of-the-art methods. In particular, it outperforms deep learning methods when tested on the Multi-PIE database.

Analysis and Approximation of Some Shape-from-Shading Models for Non-Lambertian Surfaces

The reconstruction of a 3D object or a scene is a classical inverse problem in Computer Vision. In the case of a single image this is called the Shape-from-Shading (SfS) problem and it is known to be ill-posed even in a simplified version like the vertical light source case. A huge number of works deals with the orthographic SfS problem based on the Lambertian reflectance model, the most common and simplest model which leads to an eikonal-type equation when the light source is on the vertical axis. In this paper, we want to study non-Lambertian models since they are more realistic and suitable whenever one has to deal with different kind of surfaces, rough or specular. We will present a unified mathematical formulation of some popular orthographic non-Lambertian models, considering vertical and oblique light directions as well as different viewer positions. These models lead to more complex stationary non-linear partial differential equations of Hamilton---Jacobi type which can be regarded as the generalization of the classical eikonal equation corresponding to the Lambertian case. However, all the equations corresponding to the models considered here (Oren---Nayar and Phong) have a similar structure so we can look for weak solutions to this class in the viscosity solution framework. Via this unified approach, we are able to develop a semi-Lagrangian approximation scheme for the Oren---Nayar and the Phong model and to prove a general convergence result. Numerical simulations on synthetic and real images will illustrate the effectiveness of this approach and the main features of the scheme, also comparing the results with previous results in the literature.

A lighting robust fitting approach of 3D morphable model for face reconstruction

Three-dimensional morphable model (3DMM) is a powerful tool for recovering 3D shape and texture from a single facial image. The success of 3DMM relies on two things: an effective optimization strategy and a realistic approach to synthesizing face images. However, most previous methods have focused on developing an optimization strategy under Phong's synthesis approach. In this paper, we adopt a more realistic synthesis technique that fully considers illumination and reflectance in the 3DMM fitting process. Using the sphere harmonic illumination model (SHIM), our new synthesis approach can account for more lighting factors than Phong's model. Spatially varying specular reflectance is also introduced into the synthesis process. Under SHIM, the cost function is nearly linear for all parameters, which simplifies the optimization. We apply our new optimization algorithm to determine the shape and texture parameters simultaneously. The accuracy of the recovered shape and texture can be improved significantly by considering the spatially varying specular reflectance. Hence, our algorithm produces an enhanced shape and texture compared with previous SHIM-based methods that recover shape from feature points. Although we use just a single input image in a profile pose, our approach gives plausible results. Experiments on a well-known image database show that, compared to state-of-the-art methods based on Phong's model, the proposed approach enhances the robustness of the 3DMM fitting results under extreme lighting and profile pose.

Optimal illumination directions for faces and rough surfaces for single and multiple light imaging using class-specific prior knowledge

The detection of image detail variation due to changes in illumination direction is a key issue in 3D shape and texture analysis. In this paper two approaches for estimating the optimal illumination direction for maximum enhancement of image detail and maximum suppression of shadows and highlights are presented. The methods are applicable both to single image/single illumination direction imaging and to photometric stereo imaging. This paper uses class-specific prior knowledge, where the distribution of the normals of the class of surfaces is used in the optimisation. Both the Lambertian and the Phong models are considered and the theoretical development is demonstrated with experimental results for both models. For each method experiments were performed using artificial images with isotropic and anisotropic distributions of normals, followed by experiments with real faces but synthesised images. Finally, results are presented using real objects and faces with and without ground-truth.

A Predictive Optical Simulation Model for the Rear-Surface Roughness of Passivated Silicon Solar Cells

In this paper, we introduce a predictive, physics-based model, i.e., the so-called tilted-mirror model (tm-model), for optical modeling of rough rear surfaces on silicon solar cells. An enhanced method of using transfer matrices at the rear-side interface of solar cells is developed and combined with Monte Carlo ray tracing. As a result, a physically consistent and precise simulation of the spectral reflectance is achieved, thus leading to a predictive quality of the simulations that could previously not be reached for solar cells with a remaining irregular rear-surface roughness. This advance in optical simulation enables the researcher to directly analyze the effects of varying rear-side passivation materials and thicknesses, as well as the impact of different surface morphologies on the gained charge-carrier generation rate of a solar cell. A comparison with the Phong model shows that the tm-model is able to simulate the generated photocurrent Jph more accurately, as it is shown that the Phong model tends to overestimate this value due to imprecise calculation of charge-carrier generation. In an application of the tm-model to passivated emitter and rear cells, it is shown that a strong planarization of the rear surface leads to an improvement in photogenerated current up to 0.13 mA/cm2 compared with a weak planarization.

Combination of overlap-driven adjustment and Phong model for LiDAR intensity correction

Airborne laser scanning LiDAR systems deliver not only geometric (X,Y,Z) information of the scanned surfaces but also the returned intensity of the laser pulse. Recent studies have shown the potential of using intensity data for many applications. However, there are limitations in using the raw intensity data because of radiometric system bias, reflectance noise and variations between adjacent strips. To overcome these limitations, a three-step LiDAR intensity correction algorithm is proposed. Following corrections for environmental and surface effects, an overlap-driven least-squares adjustment model that does not rely on the selection of homologous points minimizes intensity differences in the overlap area of strips. Finally, the Phong reflection model, which describes both diffuse and specular reflectance, is used to attenuate the effects of strong reflections that typically occur over wet or water dominated areas. The algorithm was applied to a multi-strip LiDAR dataset that covers wetlands in the estuary of the Yellow River, People’s Republic of China. Results demonstrated a significant reduction in radiometric differences in the overlap areas, and strong specular reflections in the nadir regions were reduced. Objects which were obscured by the specular reflection in the original intensity data were clearly identifiable after the adjustment.

A Statistical Method for SVBRDF Approximation from Video Sequences in General Lighting Conditions

AbstractWe present a statistical method for the estimation of the Spatially Varying Bidirectional Reflectance Distribution Function (SVBRDF) of an object with complex geometry, starting from video sequences acquired with fixed but general lighting conditions. The aim of this work is to define a method that simplifies the acquisition phase of the object surface appearance and allows to reconstruct an approximated SVBRDF. The final output is suitable to be used with a 3D model of the object to obtain accurate and photo‐realistic renderings. The method is composed by three steps: the approximation of the environment map of the acquisition scene, using the same object as a probe; the estimation of the diffuse color of the object; the estimation of the specular components of the main materials of the object, by using a Phong model. All the steps are based on statistical analysis of the color samples projected by the video sequences on the surface of the object. Although the method presents some limitations, the trade‐off between the easiness of acquisition and the obtained results makes it useful for practical applications.

Optical Modeling of the Rear Surface Roughness of Passivated Silicon Solar Cells

Solar cells featuring a dielectrically coated rear side combine excellent electrical passivation, yielding high open circuit voltages, with high light trapping capabilities, yielding high short circuit currents. The roughness of the rear side is a crucial parameter for the optical properties of these devices. We therefore analyze the influence of the rear surface roughness on the charge carrier generation and the spectral reflection of the devices using predictive, three dimensional modeling of the rear surface based on the transfer-matrix formalism. After the verification of this so-called tilted-mirrors-model, we compare it to the widely used Phong model. When fitting the spectral reflectance of the Phong model using its two empirical parameters to the spectral reflectance obtained with the predictive model, we calculate 0.2 - 0.4mA/cm2 lower photogenerated current densities. We further use the predictive model to demonstrate the optical impact of variations of the thickness of the passivation layers.