Analyzing and Improving the Parameterization Quality of Catmull-Clark Solids for Isogeometric Analysis.

In the field of physically based simulation, high quality of the simulation model is crucial for the correctness of the simulation results and the performance of the simulation algorithm. When working with spline or subdivision models in the context of isogeometric analysis, the quality of the parameterization has to be considered in addition to the geometric quality of the control mesh. Following Cohen et al.'s concept of model quality in addition to mesh quality, we present a parameterization quality metric tailored for Catmull-Clark (CC) solids. It measures the quality of the limit volume based on a quality measure for conformal mappings, revealing local distortions and singularities. We present topological operations that resolve these singularities by splitting certain types of boundary cells that typically occur in interactively designed CC-solid models. The improved models provide higher parameterization quality that positively affects the simulation results without additional computational costs for the solver.

Visualization System Requirements for Data Processing Pipeline Design and Optimization.

The rising quantity and complexity of data creates a need to design and optimize data processing pipelines-the set of data processing steps, parameters and algorithms that perform operations on the data. Visualization can support this process but, although there are many examples of systems for visual parameter analysis, there remains a need to systematically assess users' requirements and match those requirements to exemplar visualization methods. This article presents a new characterization of the requirements for pipeline design and optimization. This characterization is based on both a review of the literature and first-hand assessment of eight application case studies. We also match these requirements with exemplar functionality provided by existing visualization tools. Thus, we provide end-users and visualization developers with a way of identifying functionality that addresses data processing problems in an application. We also identify seven future challenges for visualization research that are not met by the capabilities of today's systems.

Practical noise reduction for progressive stochastic ray tracing with perceptual control.

A proposed method reduces noise in stochastic ray tracing for interactive progressive rendering. The method accumulates high-variance light paths in a separate buffer, which is filtered by a high-quality edge-preserving filter. Then, this method adds a combination of the noisy unfiltered samples and the less noisy (but biased) filtered samples to the low-variance samples to form the final image. A novel per-pixel blending operator combines both contributions in a way that respects a user-defined threshold on perceived noise. This method can provide fast, reliable previews, even in the presence of complex features such as specular surfaces and high-frequency textures. At the same time, it's consistent in that the bias due to filtering vanishes in the limit.

Linear algorithms in sublinear time a tutorial on statistical estimation.

This tutorial presents probability theory techniques for boosting linear algorithms. The approach is based on statistics and uses educated guesses instead of comprehensive calculations. Because estimates can be calculated in sublinear time, many algorithms can benefit from statistical estimation.