Jingui Pan

Line segment sampling with blue-noise properties

Line segment sampling has recently been adopted in many rendering algorithms for better handling of a wide range of effects such as motion blur, defocus blur and scattering media. A question naturally raised is how to generate line segment samples with good properties that can effectively reduce variance and aliasing artifacts observed in the rendering results. This paper studies this problem and presents a frequency analysis of line segment sampling. The analysis shows that the frequency content of a line segment sample is equivalent to the weighted frequency content of a point sample. The weight introduces anisotropy that smoothly changes among point samples, line segment samples and line samples according to the lengths of the samples. Line segment sampling thus makes it possible to achieve a balance between noise (point sampling) and aliasing (line sampling) under the same sampling rate. Based on the analysis, we propose a line segment sampling scheme to preserve blue-noise properties of samples which can significantly reduce noise and aliasing artifacts in reconstruction results. We demonstrate that our sampling scheme improves the quality of depth-of-field rendering, motion blur rendering, and temporal light field reconstruction.

Real-time simulating and rendering of layered dust

Detailed modeling of appearance affected by dust coverage is challenging because of the numerous physical and material factors on which it depends. The two principal roadblocks are the complexity of simulating dust particle deposition and the difficulty of accurately depicting the reflectance. In this paper, we present a practical framework for simulating and rendering the appearance of objects covered by the layered dust of spatially varying thickness. First, a fast evaluation approach is proposed to compute the dust’s thickness distribution based on a surface’s inclination and stickiness properties, as well as surface exposure and local stability. Then, to ensure high fidelity of light scattering and real-time performance, we render the scene based on a revised Kubelka–Munk model implemented on the GPU, including the effects of external and internal surface reflection. Finally, experimental results reveal that our framework can produce visually convincing dusty objects of arbitrary complexity in real time.

Rendering Thin Transparent Layers with Extended Normal Distribution Functions

Realistic Rendering of thin transparent layers bounded by rough surfaces involves substantial expense of computation time to account for multiple internal reflections. Resorting to Monte Carlo rendering for such material is usually impractical since recursive importance sampling is inevitable. To reduce the burden of sampling for simulating subsurface scattering and hence improve rendering performance, we adapt the microfacet model to the material with a single thin layer by introducing the extended normal distribution function (ENDF), a new representation of this model, to express visually perceived roughness due to multiple bounces of reflections and refractions. With such a representation, both surface reflection and subsurface scattering can be treated in the same microfacet framework, and the sampling process can be reduced to only once for each bounce of scattering. We derive analytical expressions of the ENDF for several cases using joint spherical warping. We also show how to choose proper shadowing-masking and Fresnel terms to make the proposed bidirectional scattering distribution function (BSDF) model energy-conserving. Experiments demonstrate that our model can be easily incorporated into a Monte Carlo path tracer with little extra computational and storage overhead, enabling some real-time applications.Realistic Rendering of thin transparent layers bounded by rough surfaces involves substantial expense of computation time to account for multiple internal reflections. Resorting to Monte Carlo rendering for such material is usually impractical since recursive importance sampling is inevitable. To reduce the burden of sampling for simulating subsurface scattering and hence improve rendering performance, we adapt the microfacet model to the material with a single thin layer by introducing the extended normal distribution function (ENDF), a new representation of this model, to express visually perceived roughness due to multiple bounces of reflections and refractions. With such a representation, both surface reflection and subsurface scattering can be treated in the same microfacet framework, and the sampling process can be reduced to only once for each bounce of scattering. We derive analytical expressions of the ENDF for several cases using joint spherical warping. We also show how to choose proper shadowing-masking and Fresnel terms to make the proposed bidirectional scattering distribution function (BSDF) model energy-conserving. Experiments demonstrate that our model can be easily incorporated into a Monte Carlo path tracer with little extra computational and storage overhead, enabling some real-time applications.

A retroreflective BRDF model based on prismatic sheeting and microfacet theory

Abstract We propose a novel reflectance model for the physically based rendering of retroreflective materials, based on prismatic sheeting and microfacet theory. Due to its high performance, prismatic sheeting has been extensively used in material industry. We show through a geometric optics analysis that a prismatic sheet with a smooth incident plane exhibits perfect retroreflection, and retroreflectivity varies with respect to the incident direction. To permit imperfect retroreflection that appears frequently in many common situations, we model the incident plane as a rough surface with a microfacet profile. By adjusting surface roughness, we can easily change the angularity of the retroreflection lobe. We analyze thoroughly the relationship between surface roughness and glossiness of retroreflection using joint spherical warping strategy. Based on the analysis, a practical BRDF model is derived for materials with a prismatic sheet which comprises a surface reflection term, a retroreflection term, and a diffuse reflection term.

Real-time rendering of refracting transmissive objects with multi-scale rough surfaces

This paper presents an efficient approach to render refracting transmissive objects with multi-scale surface roughness, under distant illumination. To correctly capture both fine-scale surface details and large-scale appearances, and enable real-time processing at various viewing resolutions, we first divide the surface roughness into three levels, namely, micro-scale, meso-scale and macro-scale. Each scale of roughness is modeled and evaluated using different strategies, and the overall roughness is approximated by their spherical convolution. Then, this representation is incorporated into a microfacet-based BTDF model, and multi-scale rough refractions are simulated on both front and back sides of an object as light enters and exits the object. In particular, non-linear filtering methods are applied to both macro-scale geometries and meso-scale bumps to reduce aliasing when viewed across a range of distances. Finally, experimental results illustrate that our approach produces resolution-dependent refraction effects that match super-sampled ground truth, while achieving a speed up of several orders of magnitude with hardware acceleration.

Real-Time Multi-scale Refraction under All-Frequency Environmental Lighting

We present a novel approach for real-time rendering of transparent objects with multi-scale rough refraction under all-frequency environmental lighting. In our rendering system, the normal distribution function (NDF) of the surface bumps is expressed via mixtures of von Mises-Fisher (vMF) distributions, while the corresponding perturbation of refracted rays (RDF) can be approximated as a spherical warp of NDF, taking into account correction factors. Under the assumption of two-sided refraction, we first generate the vMF lobes of the front surface refraction. Based on these lobes, we then estimate the RDF of back surface as a warped convolution of macroscopic and microscopic NDFs within the footprint of each lobe. Moreover, summed-area tables and dual-paraboloid maps are adopted to efficiently represent the environmental lighting in order to support fully dynamic lighting conditions. We finally get a coherent rendering at the different scales that reproduces high-quality meso- and micro-geometric effects under real-time performance.

Reflection Separation Using Patch-Wise Sparse and Low-Rank Decomposition

This paper introduces a robust method for removing objectionable reflection interference in photographs captured through a piece of transparent medium. We exploit the fact that a group of image patches extracted from multiple correlated images with similar transmission lie in a very low-dimensional subspace, leading to a low-rank matrix after patch assembly. This allows us to formulate reflection separation as a per-patch sparse and low-rank decomposition problem which can be well solved by the ALM-ADM strategy. To eliminate the influence of unwanted reflection in patch searching and ensure that the extracted patches has a high similarity regarding their transmission layers, we introduce a new patch similarity metric based on both image intensities and gradients. This improves the performance of reflection separation. In addition, since our method does not require image reconstruction from gradient, color-shifting artifacts can be significantly ameliorated and more scene details can be preserved. Experimental results on both synthetic images and various real-world examples demonstrate that the proposed method achieves high quality reflection separation and performs favorably against many existing techniques.

A Physically-based Appearance Model for Special Effect Pigments

An appearance model for materials adhered with massive collections of special effect pigments has to take both high‐frequency spatial details (e.g., glints) and wave‐optical effects (e.g., iridescence) due to thin‐film interference into account. However, either phenomenon is challenging to characterize and simulate in a physically accurate way. Capturing these fascinating effects in a unified framework is even harder as the normal distribution function and the reflectance term are highly correlated and cannot be treated separately. In this paper, we propose a multi‐scale BRDF model for reproducing the main visual effects generated by the discrete assembly of special effect pigments, enabling a smooth transition from fine‐scale surface details to large‐scale iridescent patterns. We demonstrate that the wavelength‐dependent reflectance inside the pixels footprint follows a Gaussian distribution according to the central limit theorem, and is closely related to the distribution of the thin‐films thickness. We efficiently determine the mean and the variance of this Gaussian distribution for each pixel whose closed‐form expressions can be derived by assuming that the thin‐films thickness is uniformly distributed. To validate its effectiveness, the proposed model is compared against some previous methods and photographs of actual materials. Furthermore, since our method does not require any scene‐dependent precomputation, the distribution of thickness is allowed to be spatially‐varying.

Automatic Background Adjustment for Chinese Paintings Using Pigment Lines

Most traditional Chinese paintings are painted on hand-made paper that easily suffers from severe spectral changes caused by prolonged light exposure, resulting in color distortion and low contrast. To recover the original appearance of a Chinese painting, especially its background color, an automatic background adjustment framework is proposed. This framework is based on the insightful observation that the fading model of a painting image is analogue to the common hazy image formation model when the painting image is transformed into the K-M (Kubelka-Munk) space. We demonstrate that this fading model is quite useful in extracting pigment lines from any painting image. These pigment lines represent clusters of distinct color pigments used in a Chinese painting, which is the key to density map estimation and background restoration. Experimental results prove that our approach is able to restore a variety of deteriorated Chinese paintings without any user intervention or training.

Importance sampling measured BRDFs based on second order spherical moment

BRDF importance sampling, which generates sampled directions in a pattern that closely matches the BRDF, is a powerful tool for variance reduction in Monte Carlo rendering. Conventionally, it is challenging to design proper sampling patterns for measured BRDFs since no analytical expression is available directly. Although tabulation based sampling strategy provides a feasible solution to this problem, it usually requires a high memory consumption for additionally storing the importance functions. In this poster, we show that the second order spherical moment of a BRDF can be leveraged in deriving a robust sampling function for the BRDF. This sampling function has an analytical form of a GGX distribution which resembles the shape of the raw BRDF measure. Besides efficient computation and compact storage brought by the GGX distribution, another benefit is its possibility to perform sampling according to the distribution of visible normals. This can further reduce the variance especially for diffuse-like materials and improve the convergence rate of the Monte Carlo numerical integration.