海量数据GPU体绘制优化研究

高性能GPU使得体绘制在廉价的硬件上获得良好的性能,但海量数据体绘制的效率依旧低下.本文探讨了GPU体绘制中图形硬件的瓶颈,并提出新颖的算法解决这些问题:采用数据分块和八叉树划分体数据实现空单元跳过优化.该算法解决了海量数据超过可用纹理空间的难题,同时允许实时改变体绘制传递函数.     

基于LOD控制与内外存调度的大规模网络态势数据节点处理算法

针对网络空间海量态势目标数据的动态、实时绘制需求,本文在细节层次(LOD)控制技术的基础上提出了一种数据分片标记与内外存动态调度相结合的态势数据处理算法。首先在底图上建立多尺度显示模型,然后将网络态势数据节点进行分层、分块标记,最后在交互时基于用户当前视口进行动态加载调度,提高内存的使用效率。经验证,该算法可以减小内存开销,提高海量态势数据绘制的实时性,提供较好的用户体验。     

基于分块混合八叉树编码的海量体视化研究

在介绍当前体数据的数据模型与绘制方法的基础上，使用混合八叉树进行体数据的描述，实现了对其自适应分块存储。并用体元投射和三维纹理映射方法分别实现了混合八叉树的体绘制与多分辨率绘制。实验结果表明，该文的算法较好地实现了基于混合八叉树结构的海量体数据的组织、存储和绘制。     

大规模地形无缝漫游技术研究*

针对大规模地形动态漫游提出实现流程和算法框架,基于分层分块地形LOD组织存储策略完成数据预处理,绘制阶段提出视点相关的地形调度和简化算法,利用多线程处理机制进行地形块裁剪和内外存数据交换,借助GPU硬件实现场景加速绘制算法,并提出分块地形和纹理数据的无缝拼接策略。真实数据实验的算法比较和性能测试结果表明,该方法具有支持数据量大,绘制效率高、实用性强等特点。     

PC平台下海量地形的分页调度和实时渲染*

结合内存调度技术和场景渲染策略,提出了一种对海量网格和纹理数据进行分割和分页调度的算法,通过调度算法使海量数据分批进入内存并实现实时卸载,在分页的基础上进行分块实时渲染。实验结果表明该算法取得了很好的效果。     

基于静态LOD的海量地形绘制中的接缝算法

为了解决海量地形分块绘制中块间接缝的问题,并在消除裂缝的同时保证地形绘制的高效性,在静态LOD算法的基础上,提出了多重边界算法.该算法把地形块边界的细节层次设置成多重的,且可以和中心三角形网脱离、合并.在绘制时,是以整个边界为单位动态地改变边界的细节层次,以实现与相邻块的无缝连接,避免每帧对边界上的点进行遍历然后重构三角形以节省了计算量.开发了软件系统,实验结果表明该算法是一种接缝效果良好,绘制效率高的算法.     

基于Geometry Clipmap的大规模地形绘制算法

针对地形渲染的实时性与大规模地形数据的海量性之间的矛盾,基于Geometry Clipmap算法的基本思想,对其进行了简化和改进.在原始地形数据处理阶段,精简了原算法的数据结构,并按Clipmap格式进行存储;在绘制阶段,针对相邻层次间的裂缝问题,提出了一种删除边的方法有效地消除了裂缝.实验表明,该方法充分利用了GPU的优势,为大规模地形的实时绘制提供了解决方案.     

基于金字塔模型的地形网格裂缝消除算法

本文针对基于多分辨金字塔模型绘制海量地形时的网格裂缝问题,提出了一种网格裂缝消除算法.该算法利用分裂标记表,结合金字塔模型本身分块与多分辨率的特性,从整体上建立相邻节点的约束关系,控制相邻节点的深度差不超过1,进而消除网格裂缝.实验结果表明,该算法可以有效的解决基于金字塔模型海量地形绘制中的网格裂缝问题.     

基于CUDA海量空间数据实时体绘制研究

针对海量空间科学数据的精细及实时三维绘制需求,提出并实现了一种基于CUDA语言的并行化光线投射体绘制加速算法,利用传统体绘制算法中光线投射法的可并行特点和GPU中高速的纹理查询的优点,通过一个实际坐标到纹理坐标的转换函数实现了对不规则采样数据的准确采样,并完成了绘制算法的CUDA并行化改造,通过CUDA语言利用GPU强大的并行计算能力实现了对海量空间数据的实时三维光线投射绘制.     

基于预计算切片序列的动态体绘制技术

为进一步提高基于硬件加速的体绘制技术的渲染性能,解决海量体绘制应用中体数据可视化环节的性能瓶颈,文中提出了一种基于硬件加速的实时切片体绘制技术的优化模型。相对于完全基于实时切片的体绘制系统,应用该技术在保证最终图像渲染质量的同时可节省动态判断切片方程的计算开销并大幅降低运行时的数据带宽需求。基于预计算的动态体绘制在切片方向选择空间中选取一组彼此正交的切片方案,并在预处理阶段生成切片几何数据,运行时根据视点位置通过特定判断算法进行方案选择。对基于分块的海量数据体绘制系统,该技术可有效降低海量体绘制的总体带宽消耗,从而进一步拓展系统的渲染规模。     

交互式虚拟内窥镜系统

计算机图形图象技术与虚拟现实技术应用于医学内窥镜系统,产生了虚拟内窥镜技术,为了将虚拟现实技术应用在医学图象处理方面,以方便医生进行虚拟手术与无创诊断,在综合利用各种计算机图形,图象技术的基础上,提出了完整的交互式虚拟内窥镜系统的框架,同时对系统结构和各种模型进行了分析和讨论,还针对系统的实时性和绘制结果的逼真性要求,提出了基于Object Cache和扩展的区域增长方法,并将其应用到医学图象处理当中,得到了较好的效果,该系统较好地解决了虚拟现实与可视化实时性和绘制精度两方面的要求,从而为医学图象可视化提供了有力的工具。     

Parallel Shear-Warp Factorization Volume Rendering Using Efficient 1-D and 2-D Partitioning Schemes for Distributed Memory Multicomputers

3-D data visualization is very useful for medical imaging and computational fluid dynamics. Volume rendering can be used to exhibit the shape and volumetric properties of 3-D objects. However, volume rendering requires a considerable amount of time to process the large volume of data. To deliver the necessary rendering rates, parallel hardware architectures such as distributed memory multicomputers offer viable solutions. The challenge is to design efficient parallel algorithms that utilize the hardware parallelism effectively. In this paper, we present two efficient parallel volume rendering algorithms, the 1D-partition and 2D-partition methods, based on the shear-warp factorization for distributed memory multicomputers. The 1D-partition method has a performance bound on the size of the volume data. If the number of processors is less than a threshold, the 1D-partition method can deliver a good rendering rate. If the number of processors is over a threshold, the 2D-partition method can be used. To evaluate the performance of these two algorithms, we implemented the proposed methods along with the slice data partitioning, volume data partitioning, and sheared volume data partitioning methods on an IBM SP2 parallel machine. Six volume data sets were used as the test samples. The experimental results show that the proposed methods outperform other compatible algorithms for all test samples. When the number of processors is over a threshold, the experimental results also demonstrate that the 2D-partition method is better than the 1D-partition method.     

Visualization of large data sets with the Active Data Repository

We implement ray-casting-based volume rendering and isosurface rendering methods using the Active Data Repository (ADR) for visualizing out-of-core data sets. We have developed the ADR object-oriented framework to provide support for applications that employ range queries with user-defined mapping and aggregation operations on large-scale multidimensional data. ADR targets distributed-memory parallel machines with one or more disks attached to each node. It is designed as a set of modular services implemented in C++, which can be customized for application-specific processing. The ADR runtime system supports common operations such as memory management, data retrieval, and scheduling of processing across a parallel machine     

Processing and interactive editing of huge point clouds from 3D scanners

This paper describes a new out-of-core multi-resolution data structure for real-time visualization, interactive editing and externally efficient processing of large point clouds. We describe an editing system that makes use of the novel data structure to provide interactive editing and preprocessing tools for large scanner data sets. Using the new data structure, we provide a complete tool chain for 3D scanner data processing, from data preprocessing and filtering to manual touch-up and real-time visualization. In particular, we describe an out-of-core outlier removal and bilateral geometry filtering algorithm, a toolset for interactive selection, painting, transformation, and filtering of huge out-of-core point-cloud data sets and a real-time rendering algorithm, which all use the same data structure as storage backend. The interactive tools work in real-time for small model modifications. For large scale editing operations, we employ a two-resolution approach where editing is planned in real-time and executed in an externally efficient offline computation afterwards. We evaluate our implementation on example data sets of sizes up to 63 GB, demonstrating that the proposed technique can be used effectively in real-world applications.     

Scalable isosurface visualization of massive datasets on commodity off-the-shelf clusters

Tomographic imaging and computer simulations are increasingly yielding massive datasets. Interactive and exploratory visualizations have rapidly become indispensable tools to study large volumetric imaging and simulation data. Our scalable isosurface visualization framework on commodity off-the-shelf clusters is an end-to-end parallel and progressive platform, from initial data access to the final display. Interactive browsing of extracted isosurfaces is made possible by using parallel isosurface extraction, and rendering in conjunction with a new specialized piece of image compositing hardware called Metabuffer. In this paper, we focus on the back end scalability by introducing a fully parallel and out-of-core isosurface extraction algorithm. It achieves scalability by using both parallel and out-of-core processing and parallel disks. It statically partitions the volume data to parallel disks with a balanced workload spectrum, and builds I/O-optimal external interval trees to minimize the number of I/O operations of loading large data from disk. We also describe an isosurface compression scheme that is efficient for progress extraction, transmission and storage of isosurfaces.     

Full body virtual autopsies using a state-of-the-art volume rendering pipeline

This paper presents a procedure for virtual autopsies based on interactive 3D visualizations of large scale, high resolution data from CT-scans of human cadavers. The procedure is described using examples from forensic medicine and the added value and future potential of virtual autopsies is shown from a medical and forensic perspective. Based on the technical demands of the procedure state-of-the-art volume rendering techniques are applied and refined to enable real-time, full body virtual autopsies involving gigabyte sized data on standard GPUs. The techniques applied include transfer function based data reduction using level-of-detail selection and multi-resolution rendering techniques. The paper also describes a data management component for large, out-of-core data sets and an extension to the GPU-based raycaster for efficient dual TF rendering. Detailed benchmarks of the pipeline are presented using data sets from forensic cases.     

交互式动态体绘制及其加速算法

体绘制三维成象法是一门新兴的3D采样数据场可视化技术，在医学成象和科学可视化领域有着极为广泛的应用，但由于3D数据量大，其使用往往受到巨大计算开销的限制，因此很多研究人员致力于静态体绘制加速算法的研究，并解决医学图象三维可视化中三维体数据显示速度与成象质量问题，因而提出了一种交互式动态体绘制算法，即从任意的视点距离和视线方向进行动态编制，并在分析其算法复杂度的基础上，提出一种新的加速算法，同时使得动态体绘制过程几乎达到实时的效果，经验证，这种算法比标准算法快4～5倍。     

Fast rendering of massive textured terrain data

This paper proposes a framework based on tile-pyramid model and linear quadtree tile-index, which enables the real-time rendering of out-of-core terrain data sets while guaranteeing geometric and texture accuracy. The Digital Elevation Model pyramid and the orthophoto pyramid are created in advance and the quadtree is used for constructing tile-index and managing data tiles. To achieve real-time loading of terrain dataset, the view frustum culling technology and the target-tiles searching algorithm based on resolution-testing and the tile-request prediction mechanism are used. While rendering the terrain tiles, the dynamic bintree triangulation is used. Experimental results show that based on the current personal computer, this framework can achieve good performance for real-time rendering of massive terrain dataset whose size is unlimited.     

Out-of-core streamline visualization on large unstructured meshes

This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk files for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that, during the streamline construction, only a very small amount of data are brought into the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is significantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-15 megabytes. We also demonstrate that this approach is much more efficient than relying on virtual memory and operating system's paging algorithms     

基于最近邻Marching Cubes的医学图像三维重建

在医学图像三维可视化中,移动立方体算法(Marching Cubes,MC)是面绘制的经典算法。针对MC算法计算插值点导致执行速度慢、效率不高的缺点,提出一种基于最近邻逼近的MC算法,该方法在n次等分点量化序列中寻找等值面最近邻点代替线性或非线性插值,既避免了插值的大量计算又保证了误差精度,还可改善三角面片结构。利用可视化工具开发包VTK对人体脸部和脚部CT数据集进行三维重建,实验表明改进算法明显缩短了绘制时间,提高了重建效率。