3DIVE: An Immersive Environment for Interactive Volume Data Exploration

This paper describes an immersive system,called 3DIVE,for interactive volume data visualization and exploration inside the CAVE virtual environment.Combining interactive volume rendering and virtual reality provides a netural immersive environment for volumetric data visualization.More advanced data exploration operations,such as object level data manipulation,simulation and analysis ,are supported in 3DIVE by several new techniques,In particular,volume primitives and texture regions ae used for the rendering,manipulation,and collision detection of volumetric objects;and the region-based rendering pipeline is integrated with 3D image filters to provide an image-based mechanism for interactive transfer function design.The system has been recently released as public domain software for CAVE/ImmersaDesk users,and is currently being actively used by various scientific and biomedical visualization projects.     

Volume-based large dynamic graph analysis supported by evolution provenance

We present an approach for the visualization and interactive analysis of dynamic graphs that contain a large number of time steps. A specific focus is put on the support of analyzing temporal aspects in the data. Central to our approach is a static, volumetric representation of the dynamic graph based on the concept of space-time cubes that we create by stacking the adjacency matrices of all time steps. The use of GPU-accelerated volume rendering techniques allows us to render this representation interactively. We identified four classes of analytics methods as being important for the analysis of large and complex graph data, which we discuss in detail: data views, aggregation and filtering, comparison, and evolution provenance. Implementations of the respective methods are presented in an integrated application, enabling interactive exploration and analysis of large graphs. We demonstrate the applicability, usefulness, and scalability of our approach by presenting two examples for analyzing dynamic graphs. Furthermore, we let visualization experts evaluate our analytics approach.

     

基于区域伸缩的空间关系表示

区域连接演算(RCC)是定性空间推理的重要基础理论之一.但由于缺乏必要的度量,RCC只是粗略地描述空间拓扑关系而难以对其更准确地描述,也难以利用RCC描述除拓扑关系之外的其它空间关系,如距离、方向等.本文在RCC理论的基础上,提出了区域伸缩演算(RESC).RESC增加了一个全等CG的原始空间关系,引入了两个新颖的对区域的演算函数,即区域延伸和区域收缩,从而给出了一种以区域为单位的形式化的度量方法.利用RESC,不仅可以扩展RCC-8拓扑关系,而且能以灵活多样的粒度来描述区域间的距离关系、方向关系、位置关系以及运动关系.RESC增强了RCC的空间关系表示能力,拓展了RCC理论的适用范围.     

Interactive transfer function design based on editing direct volume rendered images

Direct volume rendered images (DVRIs) have been widely used to reveal structures in volumetric data. However, DVRIs generated by many volume visualization techniques can only partially satisfy users' demands. In this paper, we propose a framework for editing DVRIs, which can also be used for interactive transfer function (TF) design. Our approach allows users to fuse multiple features in distinct DVRIs into a comprehensive one, to blend two DVRIs, and/or to delete features in a DVRI. We further present how these editing operations can generate smooth animations for focus + context visualization. Experimental results on some real volumetric data demonstrate the effectiveness of our method.     

Automatic Animation for Time‐Varying Data Visualization

This paper presents a digital storytelling approach that generates automatic animations for time‐varying data visualization. Our approach simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. Specifically, we analyze information related to a given event and abstract it as an event graph, which represents data features as nodes and event relationships as links. This graph embeds a tree‐like hierarchical structure which encodes data features at different scales. Next, narrative structures are built by exploring starting nodes and suitable search strategies in this graph. Different stages of narrative structures are considered in our automatic rendering parameter decision process to generate animations as digital stories. We integrate this animation generation approach into an interactive exploration process of time‐varying data, so that more comprehensive information can be provided in a timely fashion. We demonstrate with a storm surge application that our approach allows semantic visualization of time‐varying data and easy animation generation for users without special knowledge about the underlying visualization techniques.     

Extensions of parallel coordinates for interactive exploration of large multi-timepoint data sets

Parallel coordinate plots (PCPs) are commonly used in information visualization to provide insight into multi-variate data. These plots help to spot correlations between variables. PCPs have been successfully applied to unstructured datasets up to a few millions of points. In this paper, we present techniques to enhance the usability of PCPs for the exploration of large, multi-timepoint volumetric data sets, containing tens of millions of points per timestep. The main difficulties that arise when applying PCPs to large numbers of data points are visual clutter and slow performance, making interactive exploration infeasible. Moreover, the spatial context of the volumetric data is usually lost. We describe techniques for preprocessing using data quantization and compression, and for fast GPU-based rendering of PCPs using joint density distributions for each pair of consecutive variables, resulting in a smooth, continuous visualization. Also, fast brushing techniques are proposed for interactive data selection in multiple linked views, including a 3D spatial volume view. These techniques have been successfully applied to three large data sets: Hurricane Isabel (Vis'04 contest), the ionization front instability data set (Vis'08 design contest), and data from a large-eddy simulation of cumulus clouds. With these data, we show how PCPs can be extended to successfully visualize and interactively explore multi-timepoint volumetric datasets with an order of magnitude more data points.     

Interactive visualization of three-dimensional vector fields with flexible appearance control

In this paper, we present an interactive texture-based algorithm for visualizing three-dimensional steady and unsteady vector fields. The goal of the algorithm is to provide a general volume rendering framework allowing the user to compute three-dimensional flow textures interactively and to modify the appearance of the visualization on the fly. To achieve our goal, we decouple the visualization pipeline into two disjoint stages. First, flow lines are generated from the 3D vector data. Various geometric properties of the flow paths are extracted and converted into a volumetric form using a hardware-assisted slice sweeping algorithm. In the second phase of the algorithm, the attributes stored in the volume are used as texture coordinates to look up an appearance texture to generate both informative and aesthetic representations of the vector field. Our algorithm allows the user to interactively navigate through different regions of interest in the underlying field and experiment with various appearance textures. With our algorithm, visualizations with enhanced structural perception using various visual cues can be rendered in real time. A myriad of existing geometry-based and texture-based visualization techniques can also be emulated.     

地质结构三维建模及其可视化方法研究

在对前人的研究成果进行了比较分析的基础上提出了一种地质结构三维建模与可视化方法.该方法从多源数据融合角度出发,融合基础地理数据、钻孔数据、物探解译剖面数据,利用空间插值技术构建三维空间数据场,采用三维硬件纹理直接体绘制技术进行体视化,以真三维形式表达了研究区域地层结构的空间分布特征与内部属性信息.与前人研究相比,该方法反映地质结构更加准确,数据场建立速度更快.     

地质结构三维建模及其可视化方法研究*

在对前人的研究成果进行了比较分析的基础上提出了一种地质结构三维建模与可视化方法。该方法从多源数据融合角度出发,融合基础地理数据、钻孔数据、物探解译剖面数据,利用空间插值技术构建三维空间数据场,采用三维硬件纹理直接体绘制技术进行体视化,以真三维形式表达了研究区域地层结构的空间分布特征与内部属性信息。与前人研究相比,该方法反映地质结构更加准确,数据场建立速度更快。     

基于区域伸缩的空间关系研究与实现

区域连接演算(RCC)是空间推理的重要基础理论之一,它只能粗略地描述空间拓扑关系,难以描述除拓扑关系之外的其他空间关系,如距离和方向。在RCC理论的基础上,引入2个对区域的演算函数,即区域延伸和区域收缩,给出一种以区域为单位的形式化的度量方法。在RESC理论的基础上,利用栅格区域法应用简单和易于实现的特性,准确地得出区域间的空间关系。     

Interactive Volume Rendering with Dynamic Ambient Occlusion and Color Bleeding

We propose a method for rendering volumetric data sets at interactive frame rates while supporting dynamic ambient occlusion as well as an approximation to color bleeding. In contrast to ambient occlusion approaches for polygonal data, techniques for volumetric data sets have to face additional challenges, since by changing rendering parameters, such as the transfer function or the thresholding, the structure of the data set and thus the light interactions may vary drastically. Therefore, during a preprocessing step which is independent of the rendering parameters we capture light interactions for all combinations of structures extractable from a volumetric data set. In order to compute the light interactions between the different structures, we combine this preprocessed information during rendering based on the rendering parameters defined interactively by the user. Thus our method supports interactive exploration of a volumetric data set but still gives the user control over the most important rendering parameters. For instance, if the user alters the transfer function to extract different structures from a volumetric data set the light interactions between the extracted structures are captured in the rendering while still allowing interactive frame rates. Compared to known local illumination models for volume rendering our method does not introduce any substantial rendering overhead and can be integrated easily into existing volume rendering applications. In this paper we will explain our approach, discuss the implications for interactive volume rendering and present the achieved results.     

Interactive comparison of scalar fields based on largest contours with applications to flow visualization

Understanding fluid flow data, especially vortices, is still a challenging task. Sophisticated visualization tools help to gain insight. In this paper, we present a novel approach for the interactive comparison of scalar fields using isosurfaces, and its application to fluid flow datasets. Features in two scalar fields are defined by largest contour segmentation after topological simplification. These features are matched using a volumetric similarity measure based on spatial overlap of individual features. The relationships defined by this similarity measure are ranked and presented in a thumbnail gallery of feature pairs and a graph representation showing all relationships between individual contours. Additionally, linked views of the contour trees are provided to ease navigation. The main render view shows the selected features overlapping each other. Thus, by displaying individual features and their relationships in a structured fashion, we enable exploratory visualization of correlations between similar structures in two scalar fields. We demonstrate the utility of our approach by applying it to a number of complex fluid flow datasets, where the emphasis is put on the comparison of vortex related scalar quantities.     

Interactive focus + context medical data exploration and editing

Volumetric datasets are increasingly used in medical applications. In many of these applications, visualization and interaction is generally performed on cross‐sectional two‐dimensional (2D) views of three‐dimensional (3D) imaging modalities. Displaying 3D volumetric medical datasets on traditional 2D screens can present problems such as occlusion and information overload, especially when multiple data sources are present. Displaying desired information while showing the relationship to the rest of the dataset(s) can be challenging. In this paper, we present an interactive focus + context visualization approach that uses the volumetric Magic Lens interaction paradigm. We propose to use the Magic Lens as a volumetric brush to perform volume editing tasks, therefore combining data exploration with volumetric editing. Polygon‐assisted ray casting methods are used for real‐time rendering and editing frame rates, while providing compact storage of editing states for undo/redo operations. We discuss the application of our methods to radiation therapy, which is an important cancer treatment modality. We envision that this approach will improve the treatment planning process by improving the therapists' understanding of information from various sources and will help identify if the alignment of the patient in the treatment room coincides with the prepared treatment plan. Copyright © 2013 John Wiley & Sons, Ltd.     

RETRACTED ARTICLE: Texture-guided volumetric deformation and visualization using 3D moving least squares

Examining and manipulating the large volumetric data attract great interest for various applications. For such purpose, we first extend the 2D moving least squares (MLS) technique into 3D, and propose a texture-guided deformation technique for creating visualization styles through interactive manipulations of volumetric models using 3D MLS. Our framework includes focus+context (F+C) visualization for simultaneously showing the entire model after magnification, and the cut-away or illustrative visualization for providing a better understanding of anatomical and biological structures. Both visualization styles are widely applied in the graphics areas. We present a mechanism for defining features using high-dimensional texture information, and design an interface for visualizing, selecting and extracting features/objects of interest. Methods of the interactive or automatic generation of 3D control points are proposed for the flexible and plausible deformation. We describe a GPU-based implementation to achieve real-time performance of the deformation techniques and the manipulation operators. Different from physical deformation models, our framework is goal-oriented and user-guided. We demonstrate the robustness and efficiency of our framework using various volumetric datasets.     

医学体数据三维可视化技术

医学体数据的可视化是科学计算可视化的重要研究领域,其处理过程包括体数据的获取、模型的建立、数据的映射、绘制等操作。论文对医学体数据可视化的相关技术进行了综述,讨论了医学体数据的结构模型和表示方法,全面地分析了医学体数据可视化中各种算法和技术的特点,及相关的加速技术,探讨了目前医学体数据可视化存在的问题及发展趋势。     

Direct Visualization of Deformation in Volumes

Deformation is a topic of interest in many disciplines. In particular in medical research, deformations of surfaces and even entire volumetric structures are of interest. Clear visualization of such deformations can lead to important insight into growth processes and progression of disease.
We present new techniques for direct focus+context visualization of deformation fields representing transformations between pairs of volumetric datasets. Typically, such fields are computed by performing a non-rigid registration between two data volumes. Our visualization is based on direct volume rendering and uses the GPU to compute and interactively visualize features of these deformation fields in real-time. We integrate visualization of the deformation field with visualization of the scalar volume affected by the deformations. Furthermore, we present a novel use of texturing in volume rendered visualizations to show additional properties of the vector field on surfaces in the volume.     

IStar: a raster representation for scalable image and volume data

Topology has been an important tool for analyzing scalar data and flow fields in visualization. In this work, we analyze the topology of multivariate image and volume data sets with discontinuities in order to create an efficient, raster-based representation we call IStar. Specifically, the topology information is used to create a dual structure that contains nodes and connectivity information for every segmentable region in the original data set. This graph structure, along with a sampled representation of the segmented data set, is embedded into a standard raster image which can then be substantially downsampled and compressed. During rendering, the raster image is upsampled and the dual graph is used to reconstruct the original function. Unlike traditional raster approaches, our representation can preserve sharp discontinuities at any level of magnification, much like scalable vector graphics. However, because our representation is raster-based, it is well suited to the real-time rendering pipeline. We demonstrate this by reconstructing our data sets on graphics hardware at real-time rates.     

Rule‐Enhanced Transfer Function Generation for Medical Volume Visualization

In volume visualization, transfer functions are used to classify the volumetric data and assign optical properties to the voxels. In general, transfer functions are generated in a transfer function space, which is the feature space constructed by data values and properties derived from the data. If volumetric objects have the same or overlapping data values, it would be difficult to separate them in the transfer function space. In this paper, we present a rule‐enhanced transfer function design method that allows important structures of the volume to be more effectively separated and highlighted. We define a set of rules based on the local frequency distribution of volume attributes. A rule‐selection method based on a genetic algorithm is proposed to learn the set of rules that can distinguish the user‐specified target tissue from other tissues. In the rendering stage, voxels satisfying these rules are rendered with higher opacities in order to highlight the target tissue. The proposed method was tested on various volumetric datasets to enhance the visualization of important structures that are difficult to be visualized by traditional transfer function design methods. The results demonstrate the effectiveness of the proposed method.     

Opacity volume based halo generation and depth-dependent halos

Halos are generally used to enhance depth perception and display spatial relationships in illustrative visualization. In this paper, we present a simple and effective method to create volumetric halo illustration. At the preprocessing stage, we generate, on graphics hardware, a view-independent halo intensity volume, which contains all of the potential halos around the boundaries of features, based on the opacity volume. During halo rendering, the halo intensity volume is used to extract halos only around the contours of structures for the current viewpoint. The performance of our approach is significantly faster than previous halo illustration methods, which perform both halo generation and rendering during direct volume rendering. We further propose depth-dependent halo effects, including depth color fading and depth width fading. These halo effects adaptively modulate the visual properties of halos to provide more perceptual cues for depth interpretation. Experimental results demonstrate the efficiency of our proposed approach and the effectiveness of depth-dependent halos.     

iView: a feature clustering framework for suggesting informative views in volume visualization

The unguided visual exploration of volumetric data can be both a challenging and a time-consuming undertaking. Identifying a set of favorable vantage points at which to start exploratory expeditions can greatly reduce this effort and can also ensure that no important structures are being missed. Recent research efforts have focused on entropy-based viewpoint selection criteria that depend on scalar values describing the structures of interest. In contrast, we propose a viewpoint suggestion pipeline that is based on feature-clustering in high-dimensional space. We use gradient/normal variation as a metric to identify interesting local events and then cluster these via k-means to detect important salient composite features. Next, we compute the maximum possible exposure of these composite feature for different viewpoints and calculate a 2D entropy map parameterized in longitude and latitude to point out promising view orientations. Superimposed onto an interactive track-ball interface, users can then directly use this entropy map to quickly navigate to potentially interesting viewpoints where visibility-based transfer functions can be employed to generate volume renderings that minimize occlusions. To give full exploration freedom to the user, the entropy map is updated on the fly whenever a view has been selected, pointing to new and promising but so far unseen view directions. Alternatively, our system can also use a set-cover optimization algorithm to provide a minimal set of views needed to observe all features. The views so generated could then be saved into a list for further inspection or into a gallery for a summary presentation.