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Data-driven BRDF models using real material measurements have become increasingly prevalent due to the development of novel gonioreflectometers, but efficient use of these models in many graphical applications remains challenging due to the few functionalities the raw data could provide. To ameliorate this issue, we propose to analyze BRDFs using directional statistics for better handling and exploring measured materials, especially isotropic materials, with efficient computation and compact storage. We conduct a thorough statistical analysis on both analytical BRDF models and measured materials from the MERL database. We show that different aspects of visual appearance can be characterized by different spherical moments, from which several descriptive measures can be derived to further facilitate their usage. We demonstrate how these measures are best leveraged in some graphical applications including gamut mapping using a new BRDF similarity measure, BRDF or SVBRDF reconstruction based on material clustering, and importance sampling for measured materials based on fast extracted GGX distributions. We finally show the potential of our approach in the categorization of surface reflectance types which is common for traditional photon mapping.

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.

As taxi service is supervised by certain electronic equipment (e.g., global positioning system (GPS) equipment) and network technique (e.g., cab reservation through Uber in USA or DIDI in China), taxi business is a typical electronic commerce mode. For a long time, taxi service is facing a typical challenge, that is, passengers may be detoured and overcharged by some unethical taxi drivers, especially when traveling in unfamiliar cities. As a result, it is important to detect taxi drivers’ misbehavior through taxi’s GPS big data analysis in a real-time manner for enhancing the quality of taxi services. In view of this challenge, an online anomalous trajectory detection method, named OnATrade (pronounced “on a trade,” which means activities in a taxi trade on the fly), is investigated in this paper for improving taxi service using GPS big data. The method mainly consists of two steps: route recommendation and online detection. In the first step, route candidates are generated by using a route recommendation algorithm. In the second step, an online anomalous trajectory detection approach is presented to find taxis that have driving anomalies. Experiments evaluate the validity of our method on large-scale, real-world taxi GPS trajectories. Finally, several value-added applications benefiting from big data analysis over taxi’s GPS data sets are discussed for potential commercial applications.

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.

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.

ICAS is an incremental concept acquisition system using attribute-based description. It includes an algorithm for leaming concept, which induces a rule set from an example set based on the probability theory, and an algorithm for refining the rule set. This paper also introduces the learning cycles, a very useful idea of ICAS. In fact, concept acquisition by ICAS is an incremental process consisting of many such learning cycles. Also the design and implementation of ICAS are given.