基于遮挡单元裁剪的非规则数据场体绘制算法研究

通过建立一个与不透明度缓冲区同样大小的平均不透明度缓冲区,就可根据数据单元重心点在平均不透明度缓冲区内的值判定数据单元的可见性,从而使得在以物体空间为序的非规则数据场体绘制过程中直接将投影完全位于不透明区域内的数据单元裁剪掉,极大地减少需处理的数据量,加速绘制过程。     

体素分类与Phong光照模型GPU加速体绘制

为了提高体绘制的速度和效果,研究了体数据的分类和计算及其在图形处理单元上加速实现的体绘制算法.分别采用点体素先分类和预积分后分类方法为体数据分配颜色和不透明度,改进了利用不透明度对颜色加权改善绘制效果,根据Phong光照模型对体数据进行渲染.通过查找表软件加速和图形处理单元硬件加速两个方面分别实现并进行比较,实验结果表明,采用图形硬件加速的方法达到实时交互的效果,体绘制性能大大提高.     

面向医学数据的分层剥离体绘制算法

采用直接体绘制方法显示医学数据时,一般通过修改传递函数来显示感兴趣区域,但仅通过调节传递函数很难清晰地显示被遮挡的人体组织,为此提出一种新的分层剥离直接体绘制算法.通过计算相邻的不透明度极小值和极大值之间的斜率,并根据用户指定的斜率阈值和累积不透明度阈值确定分层点,实现对体数据的分层绘制.实验结果证明,该算法可以根据不同医学数据的特征信息更精确地定位分层点,绘制效果清晰,速度较快.     

基于GPU的烟雾数据体面混合绘制技术研究

研究烟雾等大规模体数据在虚拟场景中实时可视化的技术是一种新的体面混合绘制技术,在灾害现象仿真中有着重要的应用。在处理体面混合绘制时,已有的绘制算法难以实时地处理体数据的遮挡和裁剪,并在内窥绘制中出现片元空洞。针对以上问题,提出一种基于GPU的体面混合绘制算法,将体数据视为参与介质,同时将面绘制的结果作为体绘制的积分域约束条件,从而把面绘制中的裁减算法推广到体数据的绘制中,实现了体面混合绘制的无缝融合。仿真结果表明算法能够实时地绘制虚拟场景中的大规模体数据,并且能够处理复杂的内窥效果。     

利用有序数据结构实现Shear-Warp加速算法

提出一种有序数据结构,在不影响算法效果的前提下,改进并加速Shear-Warp算法的运行速度.将体数据每个层片编码成以体素值为序的有序数组,依据不透明度函数确定不透明体素所对应的体素值范围.通过对有序数组的截取,快速定位不透明体素,跳过所有的透明体素,提高绘制速度.在普通配置计算机上验证该算法,绘制过程一般在数秒内即可完成.该算法思路明晰、操作便捷,在不影响图像质量的前提下显著提高绘制速度,满足实时性的要求.     

基于概率计算模型改进的相关性层次遮挡裁剪算法

对复杂动态场景进行高效的可见性裁剪是实时绘制领域研究中的一个重要问题.围绕该问题开展工作,并针对相关性遮挡裁剪算法中的问题进行了改进.针对相关性层次遮挡裁剪算法存在冗余和不必要遮挡查询的问题,给出了一种概率计算模型.通过比较遮挡查询时间开销与绘制时间开销的数学期望,改进了相关性遮挡裁剪算法中遮挡查询的查询策略,从而进一步缩小了查询集合,使遮挡查询更加合理.实验结果表明,该算法对深度复杂度高、面片数量大的复杂动态场景有较好的裁剪效率,能够很好地满足实时绘制的要求.     

基于纹理映射与Phong光照模型的体绘制加速算法

为了提高体绘制速度，提出了一种基于纹理映射、具有Phong光照效果的体绘制加速算法．该算法是根据Phong光照模型，利用一单位球面体来仿真相同光照绘制条件下的每一个体素的反射光强，首先形成一个以法线矢量为索引值的反射光强查寻表，再应用窗值变换的加速算法来计算体素的不透明度；然后采用纹理映射的方法将体素光强值与由不透明度组成的3D数据集从物体空间投射到观察空间，再沿视方向融合为3D图象．实验表明，这种3D旋转的明暗修正保证了体绘制中3D旋转几何变换的多视角观察的交互速度．由于该算法综合了体绘制软件算法数据处理与纹理映射硬件加速的优点，并用2D纹理映射与融合的方法实现了体数据的3D重建，因而不仅降低了对计算机硬件与软件环境的要求，而且在目前通用个人计算机上即可获得近似实时的交互绘制速度和良好的3D图象品质．据研究，该算法同样适用于3D纹理映射的体绘制方法．     

一种改进的基于CUDA的纹理映射和光线投射结合的体绘制算法

针对传统的基于GPU的光线投射算法绘制效率较低的问题,利用CUDA架构的并行计算特性和对三维纹理的处理能力进行改进和优化.将体数据映射为三维纹理,利用CUDA三维数组进行存储与绑定,纹理拾取的浮点返回值利用线性滤波进行平滑.在传输函数的设计中引入中心差分梯度幅值增强对体数据边界面的绘制效果.每条光线的求交及颜色积累采用并行计算,按照由前向后进行颜色及不透明度累积.设置不透明度阈值,采用不透明度提前终止加速绘制.实验结果表明,绘制速度较传统的基于GPU算法有10％的速度提升,绘制效果也有很大的改善.     

应用混合蛙跳算法的体绘制最佳视点选择

提出一种基于混合蛙跳算法的体绘制最佳视点选择方法。利用体数据投影图像的不透明度和亮度以及提取的结构信息特征,建立反映体素重要性和体数据内部结构信息的视点评价函数;将视点评价函数作为混合蛙跳算法的适应度函数,用混合蛙跳算法来指导和优化体绘制最佳视点的选择过程,以得到全局最优视点或一组被优化的视点集。实验表明,该方法能够快速有效地聚焦和显示体数据中的重要结构信息或感兴趣区域,算法的收敛速度和收敛精度高,具有良好的全局最佳视点选择性能,能够用来指导大规模体数据场的体绘制过程。     

层次包围盒与GPU实现相结合的光线投射算法

针对目前基于GPU的光线投射算法中参数确定复杂的缺点,提出一种快速确定投射光线参数的算法,并利用层次包围盒技术对整个绘制过程进行加速.该算法利用离屏渲染技术,仅通过绘制体数据包围盒表面就能获取投射光线的参数;为了跳过对绘制结果无贡献的空体素,逐层对体数据进行分解,并生成层次包围盒树来存储对应子体数据的相关信息,通过遍历包围盒树,判断对应子体数据是否被绘制或跳过来缩短投射光线在体数据中的有效采样长度,从而实现了光线积分加速.实验结果表明,与同类算法相比,该算法预处理时间较短,在增加存储容量较小的同时获得了平均3.0的加速比,具有更好的实用性.     

Level-of-Detail Rendering of Large-Scale Irregular Volume Datasets Using Particles

This paper describes a level-of-detail rendering technique for large-scale irregular volume datasets. It is well known that the memory bandwidth consumed by visibility sorting becomes the limiting factor when carrying out volume rendering of such datasets. To develop a sorting-free volume rendering technique, we previously proposed a particle-based technique that generates opaque and emissive particles using a density function constant within an irregular volume cell and projects the particles onto an image plane with sub-pixels. When the density function changes significantly in an irregular volume cell, the cell boundary may become prominent, which can cause blocky noise. When the number of the sub-pixels increases, the required frame buffer tends to be large. To solve this problem, this work proposes a new particle-based volume rendering which generates particles using metropolis sampling and renders the particles using the ensemble average. To confirm the effectiveness of this method, we applied our proposed technique to several irregular volume datasets, with the result that the ensemble average outperforms the sub-pixel average in computational complexity and memory usage. In addition, the ensemble average technique allowed us to implement a level of detail in the interactive rendering of a 71-million-cell hexahedral volume dataset and a 26-million-cell quadratic tetrahedral volume dataset.     

通过区域块投影方法直接绘制三维数据场

直接的体绘制技术提供了在一幅图形内显示三维数据场各种信息的巨大潜力。然而，生成这样的图形是极其昂贵的，而且高质量图形的绘制远远达不到交互实现的水平。体元投方法之所以能引起人们的极大举是因为在处理过程中充分地利用了体元的空间连贯性。本文提出了一个更有效的体绘制方法：区域块投影方法（Ｂｌｏｃｋ Ｐｒｏｊｅｃｔｉｏｎ Ｍｅｔｈｏｄ）。这一算法不仅利用了体元的空间连贯性而且还充分地利用了数据场函数值分布的     

Projected slabs: approximation of perspective projection and error analysis

Virtual endoscopy is a promising medical application for volume‐rendering techniques where perspective projection is mandatory. Most of the acceleration techniques for direct volume rendering use parallel projection. This paper presents an algorithm to approximate perspective volume rendering using parallel projected slabs. The introduced error due to the approximation is investigated. An analytical study of the maximum and average error is made. This method is applied to VolumePro 500. Based on the error analysis, the basic algorithm is improved. This improvement increases the frame rate, keeping the global maximum error bounded. The usability of the algorithm is shown through the virtual endoscopic investigation of various types of medical data sets. Copyright © 2002 John Wiley & Sons, Ltd.     

High-quality splatting on rectilinear grids with efficient cullingof occluded voxels

Splatting is a popular volume rendering algorithm that pairs good image quality with an efficient volume projection scheme. The current axis-aligned sheet-buffer approach, however, bears certain inaccuracies. The effect of these is less noticeable in still images, but clearly revealed in animated viewing, where disturbing popping of object brightness occurs at certain view angle transitions. In previous work, we presented a new variant of sheet-buffered splatting in which the compositing sheets are oriented parallel to the image plane. This scheme not only eliminates the condition for popping, but also produces images of higher quality. In this paper, we summarize this new paradigm and extend it in a number of ways. We devise a new solution to render rectilinear grids of equivalent cost to the traditional approach that treats the anisotropic volume as being warped into a cubic grid. This enables us to use the usual radially symmetric kernels, which can be projected without inaccuracies. Next, current splatting approaches necessitate the projection of all voxels in the iso-interval(s), although only a subset of these voxels may eventually be visible in the final image. To eliminate these wasteful computations we propose a novel front-to-back approach that employs an occlusion map to determine if a splat contributes to the image before it is projected, thus skipping occluded splats. Additional measures are presented for further speedups. In addition, we present an efficient list-based volume traversal scheme that facilitates the quick modification of transfer functions and iso-values     

基于深度的光线投影体绘制算法

针对特征信息抽取,在传统光线投影体绘制过程中引入深度参数,提出了一种基于深度的体绘制算法.通过深度及角度交互,指导特征信息及其上下文信息的绘制.该算法将采样点深度转换成相应深度强度值,按给定方法与已知透明度值合成,得到新透明度值.在此基础上再进行颜色值合成,生成图像.给出了结果,分析了采用算法前后的效果.     

EWA splatting

We present a framework for high quality splatting based on elliptical Gaussian kernels. To avoid aliasing artifacts, we introduce the concept of a resampling filter, combining a reconstruction kernel with a low-pass filter. Because of the similarity to Heckbert's (1989) EWA (elliptical weighted average) filter for texture mapping, we call our technique EWA splatting. Our framework allows us to derive EWA splat primitives for volume data and for point-sampled surface data. It provides high image quality without aliasing artifacts or excessive blurring for volume data and, additionally, features anisotropic texture filtering for point-sampled surfaces. It also handles nonspherical volume kernels efficiently; hence, it is suitable for regular, rectilinear, and irregular volume datasets. Moreover, our framework introduces a novel approach to compute the footprint function, facilitating efficient perspective projection of arbitrary elliptical kernels at very little additional cost. Finally, we show that EWA volume reconstruction kernels can be reduced to surface reconstruction kernels. This makes our splat primitive universal in rendering surface and volume data.     

Tissue classification based on 3D local intensity structures forvolume rendering

This paper describes a novel approach to tissue classification using three-dimensional (3D) derivative features in the volume rendering pipeline. In conventional tissue classification for a scalar volume, tissues of interest are characterized by an opacity transfer function defined as a one-dimensional (1D) function of the original volume intensity. To overcome the limitations inherent in conventional 1D opacity functions, we propose a tissue classification method that employs a multidimensional opacity function, which is a function of the 3D derivative features calculated from a scalar volume as well as the volume intensity. Tissues of interest are characterized by explicitly defined classification rules based on 3D filter responses highlighting local structures, such as edge, sheet, line, and blob, which typically correspond to tissue boundaries, cortices, vessels, and nodules, respectively, in medical volume data. The 3D local structure filters are formulated using the gradient vector and Hessian matrix of the volume intensity function combined with isotropic Gaussian blurring. These filter responses and the original intensity define a multidimensional feature space in which multichannel tissue classification strategies are designed. The usefulness of the proposed method is demonstrated by comparisons with conventional single-channel classification using both synthesized data and clinical data acquired with CT (computed tomography) and MRI (magnetic resonance imaging) scanners. The improvement in image quality obtained using multichannel classification is confirmed by evaluating the contrast and contrast-to-noise ratio in the resultant volume-rendered images with variable opacity values     

Data Intermixing and Multi-volume Rendering

The main difference between multi-volume rendering and mono-volume rendering is data intermixing. In this paper, we present three levels of data intermixing and their rendering pipelines in direct multi-volume rendering, which discriminate image level intensity intermixing, accumulation level opacity intermixing, and illumination model level parameter intermixing. In the context of radiotherapy treatment planning, different data intermixing methods are applied to three volumes, including CT volume, Dose volume, and Segmentation volume, to compare the features of different data intermixing methods.