基于时空一致性的非结构化网格时变流场高效体绘制方法

时空一致性是时变流场的重要性质,也是加速时变数据可视化算法的关键.以硬件加速的光线投射算法(HRC)为框架,设计并实现了一种基于时空一致性的非结构化网格时变流场高效体绘制方法.首先提出一种分析非结构化网格单元和顶点数据时间一致性的方法,分别建立单元和顶点数据时间表,以降低绘制过程中的计算开销;然后设计一种单元和顶点数据相分离的GPU纹理结构,并采用一种小巧的单元梯度矩阵来降低显存开销;同时,设计了一种合理的数据调度策略,既能有效地避免绘制停顿,又使显存纹理结构更为紧致、高效.实验结果表明,该方法不仅明显地提高了绘制效率,而且具有更优显存空间利用率,能实现更大网格规模的非结构化网格时变流场数据体绘制.     

基于GPU的真实感毛发快速绘制

提出一种基于图形处理器(GPU)加速的真实感毛发快速绘制方法.方法通过混合绘制多层次的半透明纹理层来表示物体表面的毛发效果,并在绘制过程充分运用了GPU的可编程功能.其中采用GPU的顶点绘制器来完成多层网格层顶点位置的计算;采用像素绘制器来实现毛发特殊光照效果的计算.实验表明,通过采用GPU可编程计算,毛发的绘制速度得到了明显提高.方法对中等规模的模型达到了实时的毛发绘制速度,并具有逼真的仿真效果.     

基于NPR的3维模型线绘算法

为更好地进行3维模型的线绘,介绍了一个基于非真实感绘制技术的三角网格模型的线绘算法。该算法在预处理阶段利用离散化的曲率计算方法来估计模型顶点的平均曲率;在交互阶段,首先根据用户提供的平均曲率阈值检测模型的凹区域,同时通过设计启发式搜索算法来提取该区域适宜的线条,然后结合计算即可得到模型轮廓线,并在绘制时考虑虚拟光照效果,绘制结果表明,可以得到较满意的结果。     

一种优化绘制顺序的光线投射算法

针对光线投射算法难以满足实时性需求的问题,提出一种光线投射改进算法。该算法把梯度估计、分类与着色、明暗计算过程放置体绘制预处理阶段,减少绘制过程计算任务;简化光照模型,从前向后进行融合运算提前终止融合,避免不必要的计算量。实验结果表明该算法能有效提高光线投射算法的绘制速度。     

一种辐射度全局光照网格模型的简化方法*

全局光照模型计算通常将环境中的表面分解得足够细,以精确地捕捉由于物体间相互遮挡所引起的阴影效果及其他一些光照效果.因而,一个复杂场景经全局光照计算后,其模型复杂度远远超出了当今图形工作站的实时绘制能力.给出了一种辐射度全局光照网格模型的简化方法.算法首先根据辐射度计算的特点以及人眼的视觉特点,提出以辐射度最大相对变化值为准则,以面片合并法实现全局光照网格模型的第1步简化,将原辐射度全局光照网格模型简化为能量相对变化在用户定义误差范围内的一些超面区域.然后利用顶点删除法实现超面区域边界的简化,进一步加大原网格模型的简化程度.试验表明,这种算法不仅能有效地简化辐射度全局光照网格模型,而且能较好地保持原光照网格模型的特征.     

自适应最小梯度夹角预积分光照算法

为了增强体绘制的纹理细节与光照效果,提出了一种自适应最小梯度夹角预积分算法.通过采样点的方向导数及空间位置等信息来确定采样光线上的极值点,利用自适应划分算法加强极值点区域的绘制;为了突出物体的真实感、增强纹理细节,引入预积分光照算法,根据最小梯度夹角算法计算预积分采样段的光照颜色值,最后对预积分采样段进行体绘制积分.实验结果表明,相比传统的预积分算法,文中算法针对标量变化较大的区域能够得到较好的绘制效果,并且能够增加局部细节的光照效果.     

基于象素的光照计算技术

多年来由于硬件所提供的功能有限，实时绘制普遍采用基于顶点的光照计算技术，但是这种技术无法反映粗糙物体的光照细节，因此也就无法实时地绘制具有高度真实感细节的物体，基于象素的光照计算技术采用新一代的硬件，可以实时地绘制粗糙物体的光照细节，具有很高的真实感，首先，对基于象素的光照计算技术与基于顶点的光照计算技术进行比较，分析基于角素的光照计算技术的优点；然后，在介绍基于象素的光照计算技术的发展的同时，分析基于象素的光照计算技术的理论基础，比较了它与Phong明暗处理硬件加速技术的关系，最后，在介绍最新硬件功能的基础上，给出了实现流程和效果图。     

基于粒子群优化算法的三角网格孔洞修补

为了对三角网格模型中的复杂孔洞和曲率变化较剧烈部位处的孔洞进行修补,提出了一种基于粒子群优化算法（PSO）的三角网格孔洞修补算法。首先对孔洞多边形进行初始网格化,并计算所有网格顶点的梯度值,然后采用PSO搜索与孔洞边缘顶点梯度匹配的点集,最后根据孔洞匹配点集中顶点的梯度对孔洞中的初始网格进行修正,实现三角网格孔洞的修补。实验表明,该算法对各种复杂或曲率变化较大的孔洞,都有很好的修补效果。     

基于顶点编程的三维纹理硬件体绘制算法

为克服图形硬件对传统纹理映射体绘制的限制,提出并分析讨论了采用顶点编程来有效地实现基于纹理的体绘制中的切片组与包围盒相交过程的方法。这种新颖的技术能保证顶点处理器、片段处理器与内存宽带间工作量的平衡。同时,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。最后应用这种技术结合空区域跳跃技术,有效地去除了体数据中的空区域,降低了硬件的负载,加速了体绘制的过程。测试表明对较大规模的体数据,该算法能较大地提高性能。     

直线网格B样条混合滤波GPU光线投射

为了快速、高质量地绘制直线网格,提出B样条混合滤波方法,实现加速网格,并将其应用到直线网格GPU光线投射.证明了三次B样条基函数导数的符号性质,进而证明用快速三次滤波方法(S& H方法)计算非均匀B样条函数的导数会出现误差.据此,在光线积分计算中,如果条件允许,采用S & H方法;否则采用基于B样条基本公式的滤波方法.另外,证明三次B样条函数导数的范围,以实现梯度量调制和加速网格;在光线积分计算中,利用梯度量调制表现物质的分界面;利用加速网格,跳过无效积分步,加快绘制速度.实验结果表明,采用混合滤波的直线网格GPU光线投射方法能消除S&H方法导致的走样现象;与基于B样条基本公式的绘制方法相比,该方法更快;如果模拟或测量的物体本身是光滑的,该方法能快速反映其真实特征.     

Smooth surface extraction from unstructured point-based volume data using PDEs

Smooth surface extraction using partial differential equations (PDEs) is a well-known and widely used technique for visualizing volume data. Existing approaches operate on gridded data and mainly on regular structured grids. When considering unstructured point-based volume data where sample points do not form regular patterns nor are they connected in any form, one would typically resample the data over a grid prior to applying the known PDE-based methods. We propose an approach that directly extracts smooth surfaces from unstructured point-based volume data without prior resampling or mesh generation. When operating on unstructured data one needs to quickly derive neighborhood information. The respective information is retrieved by partitioning the 3D domain into cells using a kd-tree and operating on its cells. We exploit neighborhood information to estimate gradients and mean curvature at every sample point using a four-dimensional least-squares fitting approach. Gradients and mean curvature are required for applying the chosen PDE-based method that combines hyperbolic advection to an isovalue of a given scalar field and mean curvature flow. Since we are using an explicit time-integration scheme, time steps and neighbor locations are bounded to ensure convergence of the process. To avoid small global time steps, we use asynchronous local integration. We extract the surface by successively fitting a smooth auxiliary function to the data set. This auxiliary function is initialized as a signed distance function. For each sample and for every time step we compute the respective gradient, the mean curvature, and a stable time step. With these informations the auxiliary function is manipulated using an explicit Euler time integration. The process successively continues with the next sample point in time. If the norm of the auxiliary function gradient in a sample exceeds a given threshold at some time, the auxiliary function is reinitialized to a signed distance function. After convergence of the evolution, the resulting smooth surface is obtained by extracting the zero isosurface from the auxiliary function using direct isosurface extraction from unstructured point-based volume data and rendering the extracted surface using point-based rendering methods.     

Direct raycasting of unstructured cell-centered data by discontinuity Roe-average computation

In the field of computational fluid dynamics (CFD), the upwind finite volume method (FVM) is widely applied to solve 3D flows with discontinuity phenomena (e.g., shock waves). It produces unstructured data at the center of each cell (cell-centered data) with the flow discontinuity constraint on the inner-face between face-neighboring cells. For visualization, existing approaches with interpolation usually pre-extrapolate cell-centered data into cell-vertexed data (data values given at cell vertices) and only handle cell-vertexed data during actual rendering, which unconsciously depress the rendering accuracy and violate the discontinuity constraint. In this paper, we propose a novel method to visualize cell-centered data directly avoiding extrapolation and keep the discontinuity in the rendering data on the framework of multi-pass raycasting. During resampling, the field is reconstructed using the original cell-centered data value and the cell-gradient estimated by Green–Gauss theorem. To keep the discontinuity, we reconstruct the field at an inner-face resampled point using both the face-adjacencies and get two discontinuous field values. Then the field is obtained by computing Roe-average of the two. The analysis and experiments demonstrate that our approach gains a high-accuracy reconstruction and leads to a high-quality image.     

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.     

Progressive Real-Time Rendering of One Billion Points Without Hierarchical Acceleration Structures

Research in rendering large point clouds traditionally focused on the generation and use of hierarchical acceleration structures that allow systems to load and render the smallest fraction of the data with the largest impact on the output. The generation of these structures is slow and time consuming, however, and therefore ill-suited for tasks such as quickly looking at scan data stored in widely used unstructured file formats, or to immediately display the results of point-cloud processing tasks. We propose a progressive method that is capable of rendering any point cloud that fits in GPU memory in real time, without the need to generate hierarchical acceleration structures in advance. Our method supports data sets with a large amount of attributes per point, achieves a load performance of up to 100 million points per second, displays already loaded data in real time while remaining data is still being loaded, and is capable of rendering up to one billion points using an on-the-fly generated shuffled vertex buffer as its data structure, instead of slow-to-generate hierarchical structures. Shuffling is done during loading in order to allow efficiently filling holes with random subsets, which leads to a higher quality convergence behavior.     

Bivariate Transfer Functions on Unstructured Grids

Multi-dimensional transfer functions are commonly used in rectilinear volume renderings to effectively portray materials, material boundaries and even subtle variations along boundaries. However, most unstructured grid rendering algorithms only employ one-dimensional transfer functions. This paper proposes a novel pre-integrated Projected Tetrahedra (PT) rendering technique that applies bivariate transfer functions on unstructured grids. For each type of bivariate transfer function, an analytical form that pre-integrates the contribution of a ray segment in one tetrahedron is derived, and can be precomputed as a lookup table to compute the color and opacity in a projected tetrahedron on-the-fly. Further, we show how to approximate the integral using the pre-integration method for faster unstructured grid rendering. We demonstrate the advantages of our approach with a variety of examples and comparisons with one-dimensional transfer functions.     

Projected tetrahedra revisited: a barycentric formulation applied to digital radiograph reconstruction using higher-order attenuation functions

This paper presents a novel method for volume rendering of unstructured grids. Previously, we introduced an algorithm for perspective-correct interpolation of barycentric coordinates and computing polynomial attenuation integrals for a projected tetrahedron using graphics hardware. In this paper, we enhance the algorithm by providing a simple and efficient method to compute the projected shape (silhouette) and tessellation of a tetrahedron, in perspective and orthographic projection models. Our tessellation algorithm is published for the first time. Compared with works of other groups on rendering unstructured grids, the main contributions of this work are: 1) A new algorithm for finding the silhouette of a projected tetrahedron. 2) A method for interpolating barycentric coordinates and thickness on the faces of the tetrahedron. 3) Visualizing higher-order attenuation functions using GPU without preintegration. 4) Capability of applying shape deformations to a rendered tetrahedral mesh without significant performance loss. Our visualization model is independent of depth-sorting of the cells. We present imaging and timing results of our implementation, and an application in time-critical "2D-3D" deformable registration of anatomical models. We discuss the impact of using higher-order functions on quality and performance.     

Scalable hybrid unstructured and structured grid raycasting

This paper presents a scalable framework for real-time raycasting of large unstructured volumes that employs a hybrid bricking approach. It adaptively combines original unstructured bricks in important (focus) regions, with structured bricks that are resampled on demand in less important (context) regions. The basis of this focus+context approach is interactive specification of a scalar degree of interest (DOI) function. Thus, rendering always considers two volumes simultaneously: a scalar data volume, and the current DOI volume. The crucial problem of visibility sorting is solved by raycasting individual bricks and compositing in visibility order from front to back. In order to minimize visual errors at the grid boundary, it is always rendered accurately, even for resampled bricks. A variety of different rendering modes can be combined, including contour enhancement. A very important property of our approach is that it supports a variety of cell types natively, i.e., it is not constrained to tetrahedral grids, even when interpolation within cells is used. Moreover, our framework can handle multi-variate data, e.g., multiple scalar channels such as temperature or pressure, as well as time-dependent data. The combination of unstructured and structured bricks with different quality characteristics such as the type of interpolation or resampling resolution in conjunction with custom texture memory management yields a very scalable system.     

Volume MLS ray casting

The method of Moving Least Squares (MLS) is a popular framework for reconstructing continuous functions from scattered data due to its rich mathematical properties and well-understood theoretical foundations. This paper applies MLS to volume rendering, providing a unified mathematical framework for ray casting of scalar data stored over regular as well as irregular grids. We use the MLS reconstruction to render smooth isosurfaces and to compute accurate derivatives for high-quality shading effects. We also present a novel, adaptive preintegration scheme to improve the efficiency of the ray casting algorithm by reducing the overall number of function evaluations, and an efficient implementation of our framework exploiting modern graphics hardware. The resulting system enables high-quality volume integration and shaded isosurface rendering for regular and irregular volume data.     

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.