A single-pass GPU ray casting framework for interactive out-of-core rendering of massive volumetric datasets

We present an adaptive out-of-core technique for rendering massive scalar volumes employing single-pass GPU ray casting. The method is based on the decomposition of a volumetric dataset into small cubical bricks, which are then organized into an octree structure maintained out-of-core. The octree contains the original data at the leaves, and a filtered representation of children at inner nodes. At runtime an adaptive loader, executing on the CPU, updates a view and transfer function-dependent working set of bricks maintained on GPU memory by asynchronously fetching data from the out-of-core octree representation. At each frame, a compact indexing structure, which spatially organizes the current working set into an octree hierarchy, is encoded in a small texture. This data structure is then exploited by an efficient stackless ray casting algorithm, which computes the volume rendering integral by visiting non-empty bricks in front-to-back order and adapting sampling density to brick resolution. Block visibility information is fed back to the loader to avoid refinement and data loading of occluded zones. The resulting method is able to interactively explore multi-gigavoxel datasets on a desktop PC.     

基于自适应八叉树划分的高精度四面体可视化

利用八叉树结构将四面体数据转化为规则网格数据,能有效提高系统的交互性能.八叉树的划分层次越高,绘制效果越好,但数据的存储空间以及处理时间也将大幅增多.提出自适应的规则化表示方法来构建八叉树结构,改进原有的单一采样策略,并结合深度信息将采样结果转换成适用于GPU的八叉树纹理结构.然后采用光线投射算法来对体数据进行绘制,根据各区域深度不一的特点,提出了变步长的采样绘制策略.实验结果表明,本文方法降低了数据的空间存储量和处理时间,同时在绘制质量、绘制效率方面都得到了较大提高.     

基于八叉树快速分类的Shear-Warp交互式体绘制算法

基于八叉树的快速分类Shear-Warp算法,是三维规则数据场可视化的一种经典算法。它适用于三维重建过程中用户需要交互式地动态调整透明度变换函数,以观察三维实体的不同细节的应用场合。论文对算法进行了深入的研究和局部的优化。论文首先介绍算法原理,然后给出算法实现模型,最后给出实验结果和进一步的研究前景。     

硬件加速的大数据量自适应体绘制

利用树形结构和纹理映射技术,在普通微机上实现对大数据量体数据的实时交互.依靠八叉树结构和显卡的硬件加速功能,将体数据划分为不同精度的数据块,打破了大数据显示时显存与内存间容量和带宽的限制,通过交互策略动态遍历该树,实现对大数据量体数据多精度的绘制.实验结果表明,文中方法在普通微机上可以大于10帧/s的速度交互操纵GB级以上的体数据.该方法可有效地降低体绘制对于硬件的需求,使得在较低配置下对其交互成为可能.     

协同分布式图形硬件的混合并行体绘制

由于一般的共享存储并行机缺乏图形硬件,其上产生的3维科学计算数据,无法采用硬件加速的并行体绘制来就地进行数据可视化。为此基于本地并行机和分布式图形工作站,给出了一种混合并行绘制模型。该模型的工作原理是先将源数据存留在并行机,然后通过并行机的多处理器发布远程绘制命令流,进而通过操控工作站的图形硬件完成绘制;后期图像合成在并行机上执行,以发挥共享存储通信优势。通过负载平衡优化,并行绘制流水线有效实现了绘制、合成与显示的重叠。实验结果显示,该方法能以1024×1024图像分辨率,交互绘制并行机上的大规模数据场。     

Interactive direct rendering of trivariate B-spline scalarfunctions

This paper presents a direct rendering paradigm of trivariate B-spline functions that is able to incrementally update complex volumetric data sets in the order of millions of coefficients at interactive rates of several frames per second on modern workstations. This incremental rendering scheme can hence be employed in modeling sessions of volumetric trivariate functions, offering interactive volumetric sculpting capabilities. The rendering is conducted from a fixed viewpoint and in two phases. The first, preprocessing stage accumulates the effect that the coefficients of the trivariate function have on the pixels in the image. This preprocessing stage is conducted offline and only once per trivariate and viewing direction. The second stage conducts the actual rendering of the trivariate functions. As an example, during a volumetric sculpting operation, the artist can sculpt the volume and get a displayed feedback, in interactive rates     

基于空间跳跃的三维纹理硬件体绘制算法

本文提出了一种用于加速三维纹理硬件体绘制的空间跳跃算法。     

Volume visualization on shared memory architectures

Direct volume rendering algorithms are too computationally expensive to offer interactive frame rates when rendering large 3D medical datasets on standard workstations. This article presents an image space parallelization of an image order volume rendering algorithm aimed at shared memory multiprocessors. This parallel implementation of direct volume rendering can significantly speed up rendering times and visualize 3D datasets with speeds of several frames per second. The algorithm was implemented and evaluated on Convex SPP Exemplar and SGI Challenge multiprocessors.     

Fast Volume Rendering and Data Classification Using Multiresolution in Min‐Max Octrees

Large‐sized volume datasets have recently become commonplace and users are now demanding that volume‐rendering techniques to visualise such data provide acceptable results on relatively modest computing platforms. The widespread use of the Internet for the transmission and/or rendering of volume data is also exerting increasing demands on software providers. Multiresolution can address these issues in an elegant way. One of the fastest volume‐rendering alrogithms is that proposed by Lacroute & Levoy ¹ , which is based on shear‐warp factorisation and min‐max octrees (MMOs). Unfortunately, since an MMO captures only a single resolution of a volume dataset, this method is unsuitable for rendering datasets in a multiresolution form. This paper adapts the above algorithm to multiresolution volume rendering to enable near‐real‐time interaction to take place on a standard PC. It also permits the user to modify classification functions and/or resolution during rendering with no significant loss of rendering speed. A newly‐developed data structure based on the MMO is employed, the multiresolution min‐max octree, M ³ O, which captures the spatial coherence for datasets at all resolutions. Speed is enhanced by the use of multiresolution opacity transfer functions for rapidly determining and discarding transparent dataset regions. Some experimental results on sample volume datasets are presented.     

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.     

基于动态纹理载入的大规模数据场体绘制

为克服图形硬件对传统纹理映射体绘制的限制,提出了一种在普通PC上进行大规模数据场体绘制的有效方法。该方法中,体数据被划分为合适大小的数据块,这些数据块被动态的载入图形硬件,并利用3维纹理映射进行绘制。在整个绘制过程中,仅有一个数据块存储在图形硬件上,有效地提高了对大规模体数据的绘制能力。同时,充分利用目前PC图形硬件成熟的可编程特性,通过对梯度的实时计算来减少在传统纹理映射体绘制中巨大的内存消耗。实验结果表明,该方法在普通PC上可以对超过纹理内存容量的大规模体数据进行交互式体绘制。     

基于Shear-Warp算法的三维地震数据场体绘制

利用三维可视化软件包,采用Shear—Warp算法实现地震数据的模型可视化,并给出了具体算法流程。实验结果表明此算法可提高地震数据的体绘制速度,实现地震数据解释的实时交互式绘制,为地质勘探提供可视化依据。     

A flexible multi-volume shader framework for arbitrarily intersecting multi-resolution datasets

We present a powerful framework for 3D-texture-based rendering of multiple arbitrarily intersecting volumetric datasets. Each volume is represented by a multi-resolution octree-based structure and we use out-of-core techniques to support extremely large volumes. Users define a set of convex polyhedral volume lenses, which may be associated with one or more volumetric datasets. The volumes or the lenses can be interactively moved around while the region inside each lens is rendered using interactively defined multi-volume shaders. Our rendering pipeline splits each lens into multiple convex regions such that each region is homogenous and contains a fixed number of volumes. Each such region is further split by the brick boundaries of the associated octree representations. The resulting puzzle of lens fragments is sorted in front-to-back or back-to-front order using a combination of a view-dependent octree traversal and a GPU-based depth peeling technique. Our current implementation uses slice-based volume rendering and allows interactive roaming through multiple intersecting multi-gigabyte volumes.     

Hardware‐Assisted Projected Tetrahedra

We present a flexible and highly efficient hardware‐assisted volume renderer grounded on the original Projected Tetrahedra (PT) algorithm. Unlike recent similar approaches, our method is exclusively based on the rasterization of simple geometric primitives and takes full advantage of graphics hardware. Both vertex and geometry shaders are used to compute the tetrahedral projection, while the volume ray integral is evaluated in a fragment shader; hence, volume rendering is performed entirely on the GPU within a single pass through the pipeline. We apply a CUDA‐based visibility ordering achieving rendering and sorting performance of over 6 M Tet/s for unstructured datasets. Furthermore, as each tetrahedron is processed independently, we employ a data‐parallel solution which is neither bound by GPU memory size nor does it rely on auxiliary volume information. In addition, iso‐surfaces can be readily extracted during the rendering process, and time‐varying data are handled without extra burden.     

Real-time visualization of large volume datasets on standard PC hardware

In medical area, interactive three-dimensional volume visualization of large volume datasets is a challenging task. One of the major challenges in graphics processing unit (GPU)-based volume rendering algorithms is the limited size of texture memory imposed by current GPU architecture. We attempt to overcome this limitation by rendering only visible parts of large CT datasets. In this paper, we present an efficient, high-quality volume rendering algorithm using GPUs for rendering large CT datasets at interactive frame rates on standard PC hardware. We subdivide the volume dataset into uniform sized blocks and take advantage of combinations of early ray termination, empty-space skipping and visibility culling to accelerate the whole rendering process and render visible parts of volume data. We have implemented our volume rendering algorithm for a large volume data of 512 x 304 x 1878 dimensions (visible female), and achieved real-time performance (i.e., 3-4 frames per second) on a Pentium 4 2.4GHz PC equipped with NVIDIA Geforce 6600 graphics card ( 256 MB video memory). This method can be used as a 3D visualization tool of large CT datasets for doctors or radiologists.     

Interactive volume rendering of large sparse data sets using adaptive mesh refinement hierarchies

In this paper, we present an algorithm that accelerates 3D texture-based volume rendering of large, sparse data sets, i.e., data sets where only a traction of the voxels contain relevant information. In texture-based approaches, the rendering performance is affected by the fill-rate, the size of texture memory, and the texture I/O bandwidth. For sparse data, these limitations can be circumvented by restricting most of the rendering work to the relevant parts of the volume. In order to efficiently enclose the corresponding regions with axis-aligned boxes, we employ a hierarchical data structure, known as an AMR (adaptive mesh refinement) tree. The hierarchy is generated utilizing a clustering algorithm. A good balance is thereby achieved between the size of the enclosed volume, i.e., the amount to render in graphics hardware and the number of axis-aligned regions, i.e., the number of texture coordinates to compute in software. The waste of texture memory by the power-of-two restriction is minimized by a 3D packing algorithm which arranges texture bricks economically in memory. Compared to an octree approach, the rendering performance is significantly increased and less parameter tuning is necessary.     

针对全空子数据体的GPU体绘制

目的 体绘制是3维数据可视化的主要方法之一。用于体绘制的数据体中包含有大量的空体素,导致光线投射算法进行没有意义的重采样计算,必然降低绘制算法效率。针对全空子数据体体绘制低效问题,提出基于GPU体高效绘制方法。方法 利用八叉树数据结构组织数据,有效管理包含许多空体素的子数据体。通过绘制八叉树非全空叶子节点子数据体表面,使光线投射算法中起始和终止重采样位置更接近数据体中的可视部分,同时根据八叉树全空节点子数据体判定纹理查询结果,计算合适的跳跃步长,快速跳过八叉树中全空节点子数据体。结果 当数据体中空体素较多时,确定合适的八叉树深度,有效地跳过数据体中的空体素,减少体绘制运算量,实现对原基于体包围盒表面绘制的GPU光线投射算法的加速。结论 设计不透明度函数,凸显数据体中层位面,并将算法成功应用于地震数据可视化,取得很好应用效果。     

Direct volume rendering of unstructured tetrahedral meshes using CUDA and OpenMP

Direct volume visualization is an important method in many areas, including computational fluid dynamics and medicine. Achieving interactive rates for direct volume rendering of large unstructured volumetric grids is a challenging problem, but parallelizing direct volume rendering algorithms can help achieve this goal. Using Compute Unified Device Architecture (CUDA), we propose a GPU-based volume rendering algorithm that itself is based on a cell projection-based ray-casting algorithm designed for CPU implementations. We also propose a multicore parallelized version of the cell-projection algorithm using OpenMP. In both algorithms, we favor image quality over rendering speed. Our algorithm has a low memory footprint, allowing us to render large datasets. Our algorithm supports progressive rendering. We compared the GPU implementation with the serial and multicore implementations. We observed significant speed-ups that, together with progressive rendering, enables reaching interactive rates for large datasets.     

Using Marching Cubes on Small Machines

A simple technique to visualize the isosurfaces extracted from a cell-based volumetric dataset using the Marching Cubes algorithm is proposed. The technique exploits the intrinsic ordering of the triangles produced by the surface extraction algorithm by adopting a Back-to-Front visualization technique. The use of the technique together with the adoption of a simple shading algorithm permits the rendering of high resolution volumetric datasets in computational environments with limited capabilities in terms of memory and graphics hardware.     

Error-controlled real-time cut updates for multi-resolution volume rendering

Multi-resolution techniques are required for rendering large volumetric datasets exceeding the size of the graphics card's memory or even the main memory. The cut through the multi-resolution volume representation is defined by selection criteria based on error metrics. For GPU-based volume rendering, this cut has to fit into the graphics card's memory and needs to be continuously updated due to the interaction with the volume such as changing the area of interest, the transfer function or the viewpoint. We introduce a greedy cut update algorithm based on split-and-collapse operations for updating the cut on a frame-to-frame basis. This approach is guided by a global data-based metric based on the distortion of classified voxel data, and it takes into account a limited download budget for transferring data from main memory into the graphics card to avoid large frame rate variations. Our out-of-core support for handling very large volumes also makes use of split-and-collapse operations to generate an extended cut in the main memory. Finally, we introduce an optimal polynomial-time cut update algorithm, which maximizes the error reduction between consecutive frames. This algorithm is used to verify how close to the optimum our greedy split-and-collapse algorithm performs.