Gaussian transfer functions for multi-field volume visualization

Journal ArticleVolume rendering is a flexible technique for visualizing dense 3D volumetric datasets. A central element of volume rendering is the conversion between data values and observable quantities such as color and opacity. This process is usually realized through the use of transfer functions that are precomputed and stored in lookup tables. For multidimensional transfer functions applied to multivariate data, these lookup tables become prohibitively large. We propose the direct evaluation of a particular type of transfer functions based on a sum of Gaussians. Because of their simple form (in terms of number of parameters), these functions and their analytic integrals along line segments can be evaluated efficiently on current graphics hardware, obviating the need for precomputed lookup tables. We have adopted these transfer functions because they are well suited for classification based on a unique combination of multiple data values that localize features in the transfer function domain. We apply this technique to the visualization of several multivariate datasets (CT, cryosection) that are difficult to classify and render accurately at interactive rates using traditional approaches

Real-time rendering of physically-based cloud simulations for university undergraduate research fellows

Due to the character of the original source materials and the nature of batch digitization, quality control issues may be present in this document. Please report any quality issues you encounter to [email protected], referencing the URI of the item.Includes bibliographical references (leaves 38-39).Computers today employ simulations of physical phenomena such as wind and fire and other physical properties in many common applications, including programs meant for training and entertainment. We focus particularly on the realistic simulation of cloud formation and existence on current commercially-available computers. One of the challenges associated with this simulation is its display onto a computer screen, often referred to as rendering. We will present a brief overview of existing cloud rendering techniques and compare their effectiveness to rendering a simulation as it occurs. We will then suggest our rendering method which relies upon the use of three-dimensional textures and modified Gaussian transfer functions for the self-shadowing properties associated with clouds. We will analyze these results, focusing on frame rates and visual appearance, and then conclude by suggesting further work on this topic

Volume rendering with multidimensional peak finding

Journal ArticlePeak finding provides more accurate classification for direct volume rendering by sampling directly at local maxima in a transfer function, allowing for better reproduction of high-frequency features. However, the 1D peak finding technique does not extend to higherdimensional classification. In this work, we develop a new method for peak finding with multidimensional transfer functions, which looks for peaks along the image of the ray. We use piecewise approximations to dynamically sample in transfer function space between world-space samples. As with unidimensional peak finding, this approach is useful for specifying transfer functions with greater precision, and for accurately rendering noisy volume data at lower sampling rates. Multidimensional peak finding produces comparable image quality with order-of-magnitude better performance, and can reproduce features omitted entirely by standard classification. With no precomputation or storage requirements, it is an attractive alternative to preintegration for multidimensional transfer functions

Interactive deformation and visualization of level set surfaces using graphics hardware

technical reportDeformable isosurfaces, implemented with level-set methods, have demonstrated a great potential in visualization for applications such as segmentation, surface process- ing, and surface reconstruction. Their usefulness has been limited, however, by two problems. First, 3D level sets are relatively slow to compute. Second, their formulation usually entails several free parameters that can be difficult to tune correctly for specific applications. The second problem is compounded by the first. This paper presents a solution to these challenges by describing graphics processor (GPU) based algorithms for solving and visualizing level-set solutions at interactive rates. Our efficient GPU- based solution relies on packing the level-set isosurface data into a dynamic, sparse texture format. As the level set moves, this sparse data structure is updated via a novel GPU to CPU message passing scheme. When the level-set solver is integrated with a real-time volume renderer operating on the same p

Interactive deformation and visualization of level set surfaces using graphics hardware

Journal ArticleDeformable isosurfaces, implemented with level-set methods, have demonstrated a great potential in visualization for applications such as segmentation, surface processing, and surface reconstruction. Their usefulness has been limited, however, by their high computational cost and and reliance on significant parameter tuning. This paper presents a solution to these challenges by describing graphics processor (GPU) based algorithms for solving and visualizing levelset solutions at interactive rates. Our efficient GPU-based solution relies on packing the level-set isosurface data into a dynamic, sparse texture format. As the level set moves, this sparse data structure is updated via a novel GPU to CPU message passing scheme. When the level-set solver is integrated with a real-time volume renderer operating on the same packed format, a user can visualize and steer the deformable level-set surface as it evolves. In addition, the resulting isosurface can serve as a region-of-interest specifier for the volume renderer. This paper demonstrates the capabilities of this technology for interactive volume visualization and segmentation

Segmentation and visualization of multivariate features using feature-local distributions

Abstract. We introduce an iterative feature-based transfer function design that extracts and systematically incorporates multivariate featurelocal statistics into a texture-based volume rendering process. We argue that an interactive multivariate feature-local approach is advantageous when investigating ill-defined features, because it provides a physically meaningful, quantitatively rich environment within which to examine the sensitivity of the structure properties to the identification parameters. We demonstrate the efficacy of this approach by applying it to vortical structures in Taylor-Green turbulence. Our approach identified the existence of two distinct structure populations in these data, which cannot be isolated or distinguished via traditional transfer functions based on global distributions

Topological Galleries: A High Level User Interface for Topology Controlled Volume Rendering

Existing topological interfaces to volume rendering are limited by their reliance on sophisticated knowledge of topology by the user. We extend previous work by describing topological galleries, an interface for novice users that is based on the design galleries approach. We report three contributions: an interface based on hierarchical thumbnail galleries to display the containment relationships between topologically identifiable features, the use of the pruning hierarchy instead of branch decomposition for contour tree simplification, and drag-and-drop transfer function assignment for individual components. Initial results suggest that this approach suffers from limitations due to rapid drop-off of feature size in the pruning hierarchy. We explore these limitations by providing statistics of feature size as function of depth in the pruning hierarchy of the contour tree

Morse-Smale decomposition of multivariate transfer function space for separably-sampled volume rendering

We present a topology-guided technique for improving performance of multifield volume rendering with peak finding and preintegration with 2D transfer functions. We apply Morse-Smale decomposition to segment the multidimensional transfer function domain. This segmentation helps to reduce the number of cases where sampling in transfer function space should be performed, effectively reducing the rendering cost for equivalent sampling quality. We show that the overall performance is increased depending on the topology of a transfer function

Concept-driven visualization for terascale data analytics

Over the past couple of decades the amount of scientific data sets has exploded. The science community has since been facing the common problem of being drowned in data, and yet starved of information. Identification and extraction of meaningful features from large data sets has become one of the central problems of scientific research, for both simulation as well as sensory data sets. The problems at hand are multifold and need to be addressed concurrently to provide scientists with the necessary tools, methods, and systems. Firstly, the underlying data structures and management need to be optimized for the kind of data most commonly used in scientific research, i.e. terascale time-varying, multi-dimensional, multi-variate, and potentially non-uniform grids. This implies avoidance of data duplication, utilization of a transparent query structure, and use of sophisticated underlying data structures and algorithms.Secondly, in the case of scientific data sets, simplistic queries are not a sufficient method to describe subsets or features. For time-varying data sets, many features can generally be described as local events, i.e. spatially and temporally limited regions with characteristic properties in value space. While most often scientists know quite well what they are looking for in a data set, at times they cannot formally or definitively describe their concept well to computer science experts, especially when based on partially substantiated knowledge. Scientists need to be enabled to query and extract such features or events directly and without having to rewrite their hypothesis into an inadequately simple query language. Thirdly, tools to analyze the quality and sensitivity of these event queries itself are required. Understanding local data sensitivity is a necessity for enabling scientists to refine query parameters as needed to produce more meaningful findings.Query sensitivity analysis can also be utilized to establish trends for event-driven queries, i.e. how does the query sensitivity differ between locations and over a series of data sets. In this dissertation, we present an approach to apply these interdependent measures to aid scientists in better understanding their data sets. An integrated system containing all of the above tools and system parts is presented