3,249 research outputs found
Doctor of Philosophy
dissertationVisualization and exploration of volumetric datasets has been an active area of research for over two decades. During this period, volumetric datasets used by domain users have evolved from univariate to multivariate. The volume datasets are typically explored and classified via transfer function design and visualized using direct volume rendering. To improve classification results and to enable the exploration of multivariate volume datasets, multivariate transfer functions emerge. In this dissertation, we describe our research on multivariate transfer function design. To improve the classification of univariate volumes, various one-dimensional (1D) or two-dimensional (2D) transfer function spaces have been proposed; however, these methods work on only some datasets. We propose a novel transfer function method that provides better classifications by combining different transfer function spaces. Methods have been proposed for exploring multivariate simulations; however, these approaches are not suitable for complex real-world datasets and may be unintuitive for domain users. To this end, we propose a method based on user-selected samples in the spatial domain to make complex multivariate volume data visualization more accessible for domain users. However, this method still requires users to fine-tune transfer functions in parameter space transfer function widgets, which may not be familiar to them. We therefore propose GuideME, a novel slice-guided semiautomatic multivariate volume exploration approach. GuideME provides the user, an easy-to-use, slice-based user interface that suggests the feature boundaries and allows the user to select features via click and drag, and then an optimal transfer function is automatically generated by optimizing a response function. Throughout the exploration process, the user does not need to interact with the parameter views at all. Finally, real-world multivariate volume datasets are also usually of large size, which is larger than the GPU memory and even the main memory of standard work stations. We propose a ray-guided out-of-core, interactive volume rendering and efficient query method to support large and complex multivariate volumes on standard work stations
Visuelle Analyse groĂer Partikeldaten
Partikelsimulationen sind eine bewĂ€hrte und weit verbreitete numerische Methode in der Forschung und Technik. Beispielsweise werden Partikelsimulationen zur Erforschung der KraftstoffzerstĂ€ubung in Flugzeugturbinen eingesetzt. Auch die Entstehung des Universums wird durch die Simulation von dunkler Materiepartikeln untersucht. Die hierbei produzierten Datenmengen sind immens. So enthalten aktuelle Simulationen Billionen von Partikeln, die sich ĂŒber die Zeit bewegen und miteinander interagieren. Die Visualisierung bietet ein groĂes Potenzial zur Exploration, Validation und Analyse wissenschaftlicher DatensĂ€tze sowie der zugrundeliegenden
Modelle. Allerdings liegt der Fokus meist auf strukturierten Daten mit einer regulĂ€ren Topologie. Im Gegensatz hierzu bewegen sich Partikel frei durch Raum und Zeit. Diese Betrachtungsweise ist aus der Physik als das lagrange Bezugssystem bekannt. Zwar können Partikel aus dem lagrangen in ein regulĂ€res eulersches Bezugssystem, wie beispielsweise in ein uniformes Gitter, konvertiert werden. Dies ist bei einer groĂen Menge an Partikeln jedoch mit einem erheblichen Aufwand verbunden. DarĂŒber hinaus fĂŒhrt diese Konversion meist zu einem Verlust der PrĂ€zision bei gleichzeitig erhöhtem Speicherverbrauch. Im Rahmen dieser Dissertation werde ich neue Visualisierungstechniken erforschen, welche speziell auf der lagrangen Sichtweise basieren. Diese ermöglichen eine effiziente und effektive visuelle Analyse groĂer Partikeldaten
MFA-DVR: Direct Volume Rendering of MFA Models
3D volume rendering is widely used to reveal insightful intrinsic patterns of
volumetric datasets across many domains. However, the complex structures and
varying scales of volumetric data can make efficiently generating high-quality
volume rendering results a challenging task. Multivariate functional
approximation (MFA) is a new data model that addresses some of the critical
challenges: high-order evaluation of both value and derivative anywhere in the
spatial domain, compact representation for large-scale volumetric data, and
uniform representation of both structured and unstructured data. In this paper,
we present MFA-DVR, the first direct volume rendering pipeline utilizing the
MFA model, for both structured and unstructured volumetric datasets. We
demonstrate improved rendering quality using MFA-DVR on both synthetic and real
datasets through a comparative study. We show that MFA-DVR not only generates
more faithful volume rendering than using local filters but also performs
faster on high-order interpolations on structured and unstructured datasets.
MFA-DVR is implemented in the existing volume rendering pipeline of the
Visualization Toolkit (VTK) to be accessible by the scientific visualization
community
Model for volume lighting and modeling
Journal ArticleAbstract-Direct volume rendering is a commonly used technique in visualization applications. Many of these applications require sophisticated shading models to capture subtle lighting effects and characteristics of volumetric data and materials. For many volumes, homogeneous regions pose problems for typical gradient-based surface shading. Many common objects and natural phenomena exhibit visual quality that cannot be captured using simple lighting models or cannot be solved at interactive rates using more sophisticated methods. We present a simple yet effective interactive shading model which captures volumetric light attenuation effects that incorporates volumetric shadows, an approximation to phase functions, an approximation to forward scattering, and chromatic attenuation that provides the subtle appearance of translucency. We also present a technique for volume displacement or perturbation that allows realistic interactive modeling of high frequency detail for both real and synthetic volumetric data
SignatureSpace: a multidimensional, exploratory approach for the analysis of volume data
The analysis of volumetric data is a crucial part in the visualization
pipeline, since it determines the features in a volume dataset and
henceforth, also its rendering parameters. Unfortunately, volume
analysis can also be a very tedious and difficult challenge.
To cope with this challenge, this paper describes a novel information
visualization driven, explorative approach that allows users
to perform an analysis in a comprehensive fashion. From the original
data volume, a variety of auxiliary data volumes, the signature
volumes, are computed, which are based on intensity, gradients, and
various other statistical metrics. Each of these signatures (or signatures
in short) is then unified into a multi-dimensional signature
space to create a comprehensive scope for the analysis. A mosaic of
visualization techniques ranging from parallel coordinates, to colormaps
and opacity modulation, is available to provide insight into
the structure and feature distribution of the volume dataset, and thus
enables a specification of complex multi-dimensional transfer functions
and segmentations
Stochastic Volume Rendering of Multi-Phase SPH Data
In this paper, we present a novel method for the direct volume rendering of large smoothedâparticle hydrodynamics (SPH) simulation data without transforming the unstructured data to an intermediate representation. By directly visualizing the unstructured particle data, we avoid long preprocessing times and large storage requirements. This enables the visualization of large, timeâdependent, and multivariate data both as a postâprocess and in situ. To address the computational complexity, we introduce stochastic volume rendering that considers only a subset of particles at each step during ray marching. The sample probabilities for selecting this subset at each step are thereby determined both in a viewâdependent manner and based on the spatial complexity of the data. Our stochastic volume rendering enables us to scale continuously from a fast, interactive preview to a more accurate volume rendering at higher cost. Lastly, we discuss the visualization of freeâsurface and multiâphase flows by including a multiâmaterial model with volumetric and surface shading into the stochastic volume rendering
Abstract Feature Space Representation for Volumetric Transfer Function Exploration
The application of n-dimensional transfer functions for feature segmentation has become increasingly popular in volume rendering. Recent work has focused on the utilization of higher order dimensional transfer functions incorporating spatial dimensions (x,y, and z) along with traditional feature space dimensions (value and value gradient). However, as the dimensionality increases, it becomes exceedingly difficult to abstract the transfer function into an intuitive and interactive workspace. In this work we focus on populating the traditional two-dimensional histogram with a set of derived metrics from the spatial (x, y and z) and feature space (value, value gradient, etc.) domain to create a set of abstract feature space transfer function domains. Current two-dimensional transfer function widgets typically consist of a two-dimensional histogram where each entry in the histogram represents the number of voxels that maps to that entry. In the case of an abstract transfer function design, the amount of spatial variance at that histogram
coordinate is mapped instead, thereby relating additional information about the data abstraction in the projected space. Finally, a non-parametric kernel density estimation approach for feature space clustering is applied in the abstracted space, and the resultant transfer functions are discussed with respect to the space abstraction
Multidimensional transfer functions for interactive volume rendering
Journal ArticleAbstract-Most direct volume renderings produced today employ one-dimensional transfer functions which assign color and opacity to the volume based solely on the single scalar quantity which comprises the data set. Though they have not received widespread attention, multidimensional transfer functions are a very effective way to extract materials and their boundaries for both scalar and multivariate data. However, identifying good transfer functions is difficult enough in one dimension, let alone two or three dimensions. This paper demonstrates an important class of three-dimensional transfer functions for scalar data, and describes the application of multidimensional transfer functions to multivariate data. We present a set of direct manipulation widgets that make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to both interactively render with multidimensional transfer functions and to provide interactive shadows for volumes. The transfer functions, widgets, and hardware combine to form a powerful system for interactive volume exploration
- âŠ