Multiresolution volume visualization with a texture-based octree

Although 3D texture-based volume rendering guarantees image quality almost interactively, it is difficult to maintain an interactive rate when the technique has to be exploited on large datasets. In this paper, we propose a new texture memory representation and a management policy that substitute the classical one-texel per voxel approach for a hierarchical approach. The hierarchical approach benefits nearly homogeneous regions and regions of lower interest. The proposed algorithm is based on a simple traversal of the octree representation of the volume data. Driven by a user-defined image quality, defined as a combination of data homogeneity and importance, a set of octree nodes (the cut) is selected to be rendered. The degree of accuracy applied for the representation of each one of the nodes of the cut in the texture memory is set independently according to the user-defined parameters. The variable resolution texture model obtained reduces the texture memory size and thus texture swapping, improving rendering speed.     

大规模结构网格数据的相关性统计建模轻量化方法

高置信度的数据可视分析对于大规模数值模拟至关重要,但是当前高性能计算机的存储瓶颈导致可视分析应用获取原始高分辨率网格数据越来越困难. 基于统计建模的方法能够极大降低高分辨数据存储成本,但是重建数据的不确定性高. 为此,提出了一种大规模结构网格数据的相关性统计建模轻量化方法,用于对并行数值模拟生成的大规模多块体数据进行高效分析与可视化. 该方法的技术核心是通过数据块间的统计相关性,指导邻接数据块的统计建模,从而有效地保留数据统计特征,且不需要对不同并行计算节点中的数据块进行合并与重新分块. 通过耦合数据块的数值分布信息、空间分布信息和相关性信息,该方法可以更精确地重建原始数据,降低可视化的不确定性. 实验测试采用了最大10亿网格规模的5组科学数据,定量分析结果显示,在相同数据压缩比下,该方法相比现有方法可将数据重建精度最大提升近2个数量级.

     

Direct visualization of volume data

A combination of segmentation tools and fast volume renderers that provides an interactive exploration environment for volume visualization is discussed. The tools and renderers include mechanisms that distribute volume data across multiple processors, as well as image compositing techniques and solutions to representation problems in the selection and display of subregions within bounding volumes. A volume visualization technique using the interactive control of images rendered directly from volume data coupled with a user-controlled semantic classification tool is described. The variations of parallel volume rendering being explored on the Pixel-Planes 5 system and the region-of-interest selection methods and the interactive tools used by the system are presented. The flexibility and power of combining volume rendering with region-of-interest selection techniques are demonstrated using examples of medical imaging applications     

Multimodality visualization of medical volume data

New developments in 3-D volume acquisitions are creating a rapidly increasing demand for integrating multimodality 3-D visualization. In order to accomplish routine clinical multimodality visualization, many issues have to be dealt with, such as techniques for accurate spatial registration, integrated representation, suitable graphical user interfaces, and obtaining adequate rendering speeds. The aim of this experience paper is 2-fold. First, it presents various results from our research on multimodality visualization/registration. Second, this paper explicitly addresses practical problems and findings related to software development and multimodality registration/visualization. We hope that this will give colleagues a better understanding in some of these issues based on our experience, including notably our mistakes.     

Feature-preserving volume data reduction and focus+context visualization

The growing sizes of volumetric data sets pose a great challenge for interactive visualization. In this paper, we present a feature-preserving data reduction and focus+context visualization method based on transfer function driven, continuous voxel repositioning and resampling techniques. Rendering reduced data can enhance interactivity. Focus+context visualization can show details of selected features in context on display devices with limited resolution. Our method utilizes the input transfer function to assign importance values to regularly partitioned regions of the volume data. According to user interaction, it can then magnify regions corresponding to the features of interest while compressing the rest by deforming the 3D mesh. The level of data reduction achieved is significant enough to improve overall efficiency. By using continuous deformation, our method avoids the need to smooth the transition between low and high-resolution regions as often required by multiresolution methods. Furthermore, it is particularly attractive for focus+context visualization of multiple features. We demonstrate the effectiveness and efficiency of our method with several volume data sets from medical applications and scientific simulations.     

面向时变体数据的特征可视化方法

目的 自然界中的大部分现象本质上都是在空间上随时间的流逝不断发展变化的物理或化学过程,可以表述为含有时间变量的数据场,这些数据场称为时变体数据。随着科学计算技术、计算机仿真技术以及现代观测技术的发展,能够以前所未有的精度对自然现象进行仿真或者观测,但同时也面临时变体数据体积大、时间长以及变量数目多的难题。为了更有效地显示时变体数据并挖掘数据中的关键信息,针对时变体数据的可视化,本文提出一种基于数据特征的方法,用于探索时变体数据中感兴趣区域(即特征)的特点与变化。方法 通过将特征提取、特征跟踪、运动检测和提出的3种特征可视化方法(数据帧特征可视化、单个运动过程特征可视化和空间多运动过程特征可视化)置于同一个框架之中,提供一种从时间域和空间域探索多变量时变体数据的一站式解决方案,并突出时变体数据的动力学特性。结果 本文方法在4组不同的时变体数据上应用,对数据中特征各变量的变化以及感兴趣的运动进行了特征可视化。结论 实验结果显示本文方法能以较小的时间成本有效显示数据中的特征以及用户定义的运动,方法的有效性与实用性得到了验证。     

Scalable data servers for large multivariate volume visualization

Volumetric datasets with multiple variables on each voxel over multiple time steps are often complex, especially when considering the exponentially large attribute space formed by the variables in combination with the spatial and temporal dimensions. It is intuitive, practical, and thus often desirable, to interactively select a subset of the data from within that high-dimensional value space for efficient visualization. This approach is straightforward to implement if the dataset is small enough to be stored entirely in-core. However, to handle datasets sized at hundreds of gigabytes and beyond, this simplistic approach becomes infeasible and thus, more sophisticated solutions are needed. In this work, we developed a system that supports efficient visualization of an arbitrary subset, selected by range-queries, of a large multivariate time-varying dataset. By employing specialized data structures and schemes of data distribution, our system can leverage a large number of networked computers as parallel data servers, and guarantees a near optimal load-balance. We demonstrate our system of scalable data servers using two large time-varying simulation datasets.     

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.     

基于GPU光线投射算法的心脏体数据三维可视化

心脏成像和可视化技术在心脏疾病诊断、治疗规划中发挥着重要作用。针对分割后的心脏体数据集,提出了使用图形处理器(GPU)加速光线投射算法进行高质量三维可视化的新方法。该方法结合心脏体数据统计信息设计传递函数,增大细微组织的不透明度值;基于梯度模自适应地调整采样步长,提高组织边界的采样频率;应用改进的Blinn-Phong多光源光照模型,增强可视化效果。实验结果表明,该方法在实现实时绘制的同时,能够获得高质量的体绘制效果,渲染出清晰的瓣膜和冠状动脉血管等心脏细微组织。     

Surface extraction from multi-field particle volume data using multi-dimensional cluster visualization

Data sets resulting from physical simulations typically contain a multitude of physical variables. It is, therefore, desirable that visualization methods take into account the entire multi-field volume data rather than concentrating on one variable. We present a visualization approach based on surface extraction from multi-field particle volume data. The surfaces segment the data with respect to the underlying multi-variate function. Decisions on segmentation properties are based on the analysis of the multi-dimensional feature space. The feature space exploration is performed by an automated multi-dimensional hierarchical clustering method, whose resulting density clusters are shown in the form of density level sets in a 3D star coordinate layout. In the star coordinate layout, the user can select clusters of interest. A selected cluster in feature space corresponds to a segmenting surface in object space. Based on the segmentation property induced by the cluster membership, we extract a surface from the volume data. Our driving applications are Smoothed Particle Hydrodynamics (SPH) simulations, where each particle carries multiple properties. The data sets are given in the form of unstructured point-based volume data. We directly extract our surfaces from such data without prior resampling or grid generation. The surface extraction computes individual points on the surface, which is supported by an efficient neighborhood computation. The extracted surface points are rendered using point-based rendering operations. Our approach combines methods in scientific visualization for object-space operations with methods in information visualization for feature-space operations.     

Multiresolution representation for curves based on ternary subdivision schemes

Subdivision offers a way to increase the resolution of models, while reverse subdivision possesses the opposite ability. Combining the two theories could realize the multiresolution (MR) representation of models. Based on two ternary subdivision schemes, we present the trial and refined filters and an algorithm to realize MR representation for curves, which has some difference compared with the work relating to binary schemes. And the filters yield biorthogonal wavelet systems which are the underlying theory fundament of curves MR. By experiments and numerical calculations, we demonstrate that by using the ternary methods one can accomplish the MR representation for curves and the low-resolution results obtained by reverse subdivision can approximate the original curves well. Besides, ternary methods need smaller number of decomposition times than binary methods to get low-resolution results at similar levels of resolution for the same original curve.     

3D visualization of tomographic volume data using the generalized voxel model

Multi-slice images obtained from computer tomography and magnetic resonance imaging represent a 3D image volume. For its visualization we use a raycasting algorithm working on a gray-scale voxel data model. This model is extended by additional attributes such as membership to an organ or a second imaging modality (generalized voxel model). It is shown that the combination of different surface-rendering algorithms together with cutting and transparent display allow a realistic visualization of the human anatomy.The investigations were supported in part by the Deutsche Forschungsgemeinschaft and the Werner Otto Foundation, Hamburg.     

Distributed volume visualization: A step towards integrated data analysis and image synthesis

Integrated on-the-fly data analysis and image synthesis is one of the most dominant challenges on modern volume visualization tools. Since volume visualization algorithms are them self computationally complex and memory intensive there is hardly a chance to efficiently integrate data analysis tools on standard single processor architectures. The development and spreading of multiprocessor systems with large scale memory allow for the direct analysis and modification of the data during the rendering process. But to efficiently integrate both tasks similar parallelization strategies and a unique data layout must be chosen. Furthermore, since more and more systems with hardware assisted processing and visualization options can be accessed across high performance networks, distributed visualization tools should benefit from these options whenever possible. In the following paper a prototyped parallel visualization environment will be exemplified in which data analysis and image synthesis are simultaneously performed on demand. The objective is to outline basic parallelization and distribution aspects which enable flexible integration of different tasks rather than to present a concrete implementation. In this context the development and design of a unique algorithmic framework for integrated and/or distributed data analysis and image synthesis dominates the work hereafter.     

Direct interval volume visualization

We extend direct volume rendering with a unified model for generalized isosurfaces, also called interval volumes, allowing a wider spectrum of visual classification. We generalize the concept of scale-invariant opacity—typical for isosurface rendering—to semi-transparent interval volumes. Scale-invariant rendering is independent of physical space dimensions and therefore directly facilitates the analysis of data characteristics. Our model represents sharp isosurfaces as limits of interval volumes and combines them with features of direct volume rendering. Our objective is accurate rendering, guaranteeing that all isosurfaces and interval volumes are visualized in a crack-free way with correct spatial ordering. We achieve simultaneous direct and interval volume rendering by extending preintegration and explicit peak finding with data-driven splitting of ray integration and hybrid computation in physical and data domains. Our algorithm is suitable for efficient parallel processing for interactive applications as demonstrated by our CUDA implementation.     

Particle-based non-photorealistic volume visualization

Non-photorealistic techniques are usually applied to produce stylistic renderings. In visualization, these techniques are often able to simplify data, producing clearer images than traditional visualization methods. We investigate the use of particle systems for visualizing volume datasets using non-photorealistic techniques. In our VolumeFlies framework, user-selectable rules affect particles to produce a variety of illustrative styles in a unified way. The techniques presented do not require the generation of explicit intermediary surfaces.     

Multiresolution load balancing in curved space: the wavelet representation

A new load-balancing scheme based on a multiresolution analysis is developed for parallel particle simulations. Workloads are partitioned with a uniform 3-dimensional mesh in an adaptive curvilinear co-ordinate system which is represented by a wavelet basis. Simulated annealing is used to determine the optimal wavelet coefficients which minimize load imbalance and communication costs. Performance tests on a parallel computer involving up to 1.04 billion particles demonstrate the scalability of the new load balancer. Copyright © 1999 John Wiley & Sons, Ltd.     

Interactive clipping techniques for texture-based volume visualization and volume shading

We propose clipping methods that are capable of using complex geometries for volume clipping. The clipping tests exploit per-fragment operations on the graphics hardware to achieve high frame rates. In combination with texture-based volume rendering, these techniques enable the user to interactively select and explore regions of the data set. We present depth-based clipping techniques that analyze the depth structure of the boundary representation of the clip geometry to decide which parts of the volume have to be clipped. In another approach, a voxelized clip object is used to identify the clipped regions. Furthermore, the combination of volume clipping and volume shading is considered. An optical model is introduced to merge aspects of surface-based and volume-based illumination in order to achieve a consistent shading of the clipping surface. It is demonstrated how this model can be efficiently incorporated in the aforementioned clipping techniques.     

Towards animating ray-traced volume visualization

Ray-tracing volumetric data may take several minutes to compute a single image from a fixed viewpoint. We present techniques that generate approximate ray-traced volumetric images in less than one second per image, after a lengthy initialization process is performed. These approximate images are based on methods that interpolate data sampled at locations on a sphere.     

Saliency-guided enhancement for volume visualization

Recent research in visual saliency has established a computational measure of perceptual importance. In this paper we present a visual-saliency-based operator to enhance selected regions of a volume. We show how we use such an operator on a user-specified saliency field to compute an emphasis field. We further discuss how the emphasis field can be integrated into the visualization pipeline through its modifications of regional luminance and chrominance. Finally, we validate our work using an eye-tracking-based user study and show that our new saliency enhancement operator is more effective at eliciting viewer attention than the traditional Gaussian enhancement operator.     

Research issues in volume visualization

Volume visualization is a method of extracting meaningful information from volumetric data sets through the use of interactive graphics and imaging. It addresses the representation, manipulation, and rendering of volumetric data sets, providing mechanisms for peering into structures and understanding their complexity and dynamics. Typically, the data set is represented as a 3D regular grid of volume elements (voxels) and stored in a volume buffer (also called a cubic frame buffer), which is a large 3D array of voxels. However, data is often defined at scattered or irregular locations that require using alternative representations and rendering algorithms. There are eight major research issues in volume visualization: volume graphics, volume rendering, transform coding of volume data, scattered data, enriching volumes with knowledge, segmentation, real-time rendering and parallelism, and special purpose hardware