Limitations of some of the approximate structural analysis methods that are used in structural system reliability

Structural system reliability often involves structures with non-linear behavior. In the reliability analysis, simplified structural models and simplified analysis procedures are used to model and analyze these structures. These simplified analysis procedures, for both frames and trusses, have their limitations. These limitations are illustrated in this paper through deterministic examples. It is recommended that further research be done on understanding the limitations of these methods and their effect on structural reliability analysis.     

Practical Real‐Time Lens‐Flare Rendering

We present a practical real‐time approach for rendering lens‐flare effects. While previous work employed costly ray tracing or complex polynomial expressions, we present a coarser, but also significantly faster solution. Our method is based on a first‐order approximation of the ray transfer in an optical system, which allows us to derive a matrix that maps lens flare‐producing light rays directly to the sensor. The resulting approach is easy to implement and produces physically‐plausible images at high framerates on standard off‐the‐shelf graphics hardware.     

Vega: Non‐Linear FEM Deformable Object Simulator

This practice and experience paper describes a robust C++ implementation of several non‐linear solid three‐dimensional deformable object strategies commonly employed in computer graphics, named the Vega finite element method (FEM) simulation library. Deformable models supported include co‐rotational linear FEM elasticity, Saint–Venant Kirchhoff FEM model, mass–spring system and invertible FEM models: neo‐Hookean, Saint–Venant Kirchhoff and Mooney–Rivlin. We provide several timestepping schemes, including implicit Newmark and backward Euler integrators, and explicit central differences. The implementation of material models is separated from integration, which makes it possible to employ our code not only for simulation, but also for deformable object control and shape modelling. We extensively compare the different material models and timestepping schemes. We provide practical experience and insight gained while using our code in several computer animation and simulation research projects.     

Lighting Simulation of Augmented Outdoor Scene Based on a Legacy Photograph

We propose a novel approach to simulate the illumination of augmented outdoor scene based on a legacy photograph. Unlike previous works which only take surface radiosity or lighting related prior information as the basis of illumination estimation, our method integrates both of these two items. By adopting spherical harmonics, we deduce a linear model with only six illumination parameters. The illumination of an outdoor scene is finally calculated by solving a linear least square problem with the color constraint of the sunlight and the skylight. A high quality environment map is then set up, leading to realistic rendering results. We also explore the problem of shadow casting between real and virtual objects without knowing the geometry of objects which cast shadows. An efficient method is proposed to project complex shadows (such as tree's shadows) on the ground of the real scene to the surface of the virtual object with texture mapping. Finally, we present an unified scheme for image composition of a real outdoor scene with virtual objects ensuring their illumination consistency and shadow consistency. Experiments demonstrate the effectiveness and flexibility of our method.     

Noise‐Adaptive Shape Reconstruction from Raw Point Sets

We propose a noise‐adaptive shape reconstruction method specialized to smooth, closed shapes. Our algorithm takes as input a defect‐laden point set with variable noise and outliers, and comprises three main steps. First, we compute a novel noise‐adaptive distance function to the inferred shape, which relies on the assumption that the inferred shape is a smooth submanifold of known dimension. Second, we estimate the sign and confidence of the function at a set of seed points, through minimizing a quadratic energy expressed on the edges of a uniform random graph. Third, we compute a signed implicit function through a random walker approach with soft constraints chosen as the most confident seed points computed in previous step.     

Fast and Robust Approximation of Smallest Enclosing Balls in Arbitrary Dimensions

In this paper, an algorithm is introduced that computes an arbitrarily fine approximation of the smallest enclosing ball of a point set in any dimension. This operation is important in, for example, classification, clustering, and data mining. The algorithm is very simple to implement, gives reliable results, and gracefully handles large problem instances in low and high dimensions, as confirmed by both theoretical arguments and empirical evaluation. For example, using a CPU with eight cores, it takes less than two seconds to compute a 1.001‐approximation of the smallest enclosing ball of one million points uniformly distributed in a hypercube in dimension 200. Furthermore, the presented approach extends to a more general class of input objects, such as ball sets.     

Exponential Soft Shadow Mapping

In this paper we present an image‐based algorithm to render visually plausible anti‐aliased soft shadows in real time. Our technique employs a new shadow pre‐filtering method based on an extended exponential shadow mapping theory. The algorithm achieves faithful contact shadows by adopting an optimal approximation to exponential shadow reconstruction function. Benefiting from a novel overflow free summed area table tile grid data structure, numerical stability is guaranteed and error filtering response is avoided. By integrating an adaptive anisotropic filtering method, the proposed algorithm can produce high quality smooth shadows both in large penumbra areas and in high frequency sharp transitions, meanwhile guarantee cheap memory consumption and high performance.     

Bertin was Right: An Empirical Evaluation of Indexing to Compare Multivariate Time‐Series Data Using Line Plots

Line plots are very well suited for visually representing time‐series. However, several difficulties arise when multivariate heterogeneous time‐series data is displayed and compared visually. Especially, if the developments and trends of time‐series of different units or value ranges need to be compared, a straightforward overlay could be visually misleading. To mitigate this, visualization pioneer Jacques Bertin presented a method called indexing that transforms data into comparable units for visual representation. In this paper, we want to provide empirical evidence for this method and present a comparative study of the three visual comparison methods linear scale with juxtaposition, log scale with superimposition and indexing. Although for task completion times, indexing only shows slight advantages, the results support the assumption that the indexing method enables the user to perform comparison tasks with a significantly lower error rate. Furthermore, a post‐test questionnaire showed that the majority of the participants favour the indexing method over the two other comparison methods.