基于后处理的实时景深模拟与应用

针对虚拟现实系统中的景深模拟问题,提出了一种改进的基于图形处理器(GPU)的后处理景深模拟算法.该算法在场景渲染时将全分辨率纹理图存入离屏缓存区,利用该离屏缓存区的Alpha通道输出每个像素的线性化深度信息;对这个全渲染的场景纹理图作下采样处理,得到原图像1/16大小的图像;对下采样后的场景纹理进行可分离二维高斯滤波,生成模糊的场景纹理图;通过泊松采样方法,在弥散圈内将两幅纹理图基于线性化深度信息进行融合,模拟出了景深效果.最后将该算法应用于一个海上搜救虚拟现实系统.实验结果表明,所提算法较好地仿真了景深效果,改善了传统后处理滤波算法的深度连续性差、亮度扩散等问题,并能满足实时交互需求.     

面向移动设备的3D图形处理器设计

提出一种面向移动设备的3D图形处理器的设计方法,从图形算法和硬件架构两个层次进行优化.对图形算法进行C语言的仿真模拟,并设计高效的具有并行和流水线结构的图形处理器架构.该架构采用定点的数据通道,拥有一个可编程的顶点处理器和基于像素块的光栅扫描转换模块,降低电路复杂度的同时提高了整体性能.该设计已经在FPGA上验证,并给出了实验结果.实验结果显示该图形处理器结构可以满足移动设备的图形应用要求,具有可行性.     

基于图形处理器的可变形部件模型算法的并行化

目前目标识别领域,在人体检测中精确度最高的算法就是可变形部件模型(DPM)算法,针对DPM算法计算量大的缺点,提出了一种基于图形处理器(GPU)的并行化解决方法.采用GPU编程模型OpenCL,对DPM算法的整个算法的实现细节采用了并行化的思想进行重新设计实现,优化算法实现的内存模型和线程分配.通过对OpenCV库和采用GPU重新实现的程序进行对比,在保证了检测效果的前提下,使得算法的执行效率有了近8倍的提高.     

GPU上的非侵入式风格化渲染

提出一种基于硬件加速的算法,在实时图形应用中非侵入式地获得各种风格化渲染特效.通过实时地截获OpenGL API函数调用,修改了常规的渲染流程.该算法完全采用硬件加速的方法,在图形处理器中对颜色缓冲区和深度缓冲区进行后处理;同时采用OpenGL绘制语言作为高级绘制语言,从而可以和其他硬件加速算法（如置换式贴图、矩阵调色盘变形等）完全兼容.实验结果表明：文中算法适用于交互式非真实感渲染的应用,可以作为一种风格化渲染的强有力的工具.     

点模型的快速高质量绘制

针对点模型的快速高质量绘制问题,提出一种单遍绘制算法.首先根据构造的移动最小二乘曲面计算采样点的表面几何属性,然后根据协方差分析确定点元在其切平面上的椭圆表示,最后采用椭圆加权平均滤波从远到近单遍绘制各点元.此外,根据表面几何属性确定点模型的轮廓线,实现了点模型的非真实感绘制.实验结果表明,该算法能够实现点模型的快速绘制且绘制效果令人满意.     

基于CUDA 渲染器的顺序独立透明现象的单遍高效绘制

提出一种顺序独立透明现象的单遍高效绘制算法.首先设计了一个基于计算统一设备架构(compute unified device architecture,简称CUDA)的可编程渲染器.该系统采用扫描线算法光栅化场景,为每个像素生成多个对应的片元,同时,在GPU(graphics processing unit)的全局内存上为每个像素分配一个数组,以存储其相应的片元.基于这个框架,提出了两种并发的片元收集及排序策略,以单遍高效地绘制顺序独立的透明现象.第1种策略利用CUDA的原子操作符atomicMin收集各个像素上对应的所有片元并按深度动态排序,在后处理中片元即可按序逐一融合;第2种策略采用CUDA的原子操作符atomicInc按光栅化顺序收集所有片元,然后在后处理中按深度排序后再逐一融合.实验结果表明,与基于传统图形管线的经典深度剥离方法相比,该方法可以更高效地绘制顺序独立的透明现象,同时生成正确的绘制效果.     

基于宇宙计算的图形处理器算法实现

下一代观测望远镜将会产生数以亿计的星系测量数据值,这将导致使用中央处理器处理数据时效率低下、成本较高。为了解决这一问题,提出了基于宇宙计算的图形处理器算法。研究了两点式角相关函数以及孔径质量统计这两种宇宙学的计算方法,构建算法代码,并使用统一计算设备架构在图形处理器上实现了这两种算法;比较了算法在中央处理器和图形处理器上使用的运行速度。实验结果表明,与中央处理器相比,使用图形处理器的计算速度得到了显著提高。     

用GPU加速求解线性方程组的高斯消元法

提出了应用图形处理器(GPU)加速求解线性方程组的高斯消元法,用二维四通道纹理表示系数矩阵与常数向量构成的矩阵,在该矩阵内完成归一化、消元等操作.提出了新的纹理缩减算法,该算法不要求纹理的边长是2的幂,把该纹理算法应用于高斯消元法的列主元搜索和确定主元行号.根据这些算法,使用OpenGL着色语言编程,用图形处理器实现加速求解线性方程组的高斯消元法,运算时间与基于CPU的算法比较,随着方程组未知量数量增多,基于GPU的算法具有较快的运算速度,证实图形处理器能加速线性方程组的求解.     

虚拟肩关节镜手术中软组织形变的模拟

为实现虚拟肩关节镜手术中软组织的形变模拟,提出一种改进的质点-弹簧模型.通过自适应采样及K阶最邻近节点快速查询算法,建立表面质点密度高而中心质点密度低的拓扑结构模型,在传统质点-弹簧形变模型的基础上,增加防止弹簧翻转的复原力,采用计算统一设备构架实现图形处理器的加速.模拟结果表明,该模型能够实现较真实的形变模拟.     

使用GPU编程的光线投射体绘制算法

将传统的光线投射体绘制算法在具有可编程管线的图形处理器(GPU)上重新实现.首先将体数据作为三维纹理保存在显存中,然后通过编写顶点程序和片段程序将光线进入点/离开点计算和光线遍历的计算移入GPU中执行,最后根据不同的采样点颜色混合公式实现不同的绘制效果.文中算法仅需绘制一个四边形即可完成三维重建.实验结果表明:在进行光照效果的重建时,该算法能够达到实时交互的绘制要求,并能实现半透明等复杂绘制效果.     

Photo-consistent synthesis of motion blur and depth-of-field effects with a real camera model

Depth-of-field (DOF) and motion blur are important visual cues used for computer graphics and photography to illustrate focus of attention and object motion. In this work, we present a method for photo-realistic DOF and motion blur generation based on the characteristics of a real camera system. Both the depth–blur relation for different camera focus settings and the nonlinear intensity response of image sensors are modeled. The camera parameters are calibrated and used for defocus and motion blur synthesis. For a well-focused real scene image, DOF and motion blur effects are generated by post-processing techniques. Experiments have shown that the proposed method generates more photo-consistent results than the commonly used graphical models.     

基于M22图形处理器的显示驱动软件设计

为了在VxWorks操作系统下实现M22图形处理器的功能,提出了一种基于M22图形处理器的显示驱动软件的设计方法。该方法中包括了基于M22图形处理器的显示驱动软件的主要设计思路和实现过程。该方法采用风河公司提供的驱动程序框架和开发工具,为了图形应用程序提供了丰富的调用接口,并在驱动程序的编写过程中针对M22图形处理器进行相应的优化,从而保证图形处理器功能的充分发挥。该方法已经投入应用,在应用过程中取得了良好的效果。     

Optimized OpenCL implementation of the Elastodynamic Finite Integration Technique for viscoelastic media

Development of parallel codes that are both scalable and portable for different processor architectures is a challenging task. To overcome this limitation we investigate the acceleration of the Elastodynamic Finite Integration Technique (EFIT) to model 2-D wave propagation in viscoelastic media by using modern parallel computing devices (PCDs), such as multi-core CPUs (central processing units) and GPUs (graphics processing units). For that purpose we choose the industry open standard Open Computing Language (OpenCL) and an open-source toolkit called PyOpenCL. The implementation is platform independent and can be used on AMD or NVIDIA GPUs as well as classical multi-core CPUs. The code is based on the Kelvin–Voigt mechanical model which has the gain of not requiring additional field variables. OpenCL performance can be in principle, improved once one can eliminate global memory access latency by using local memory. Our main contribution is the implementation of local memory and an analysis of performance of the local versus the global memory using eight different computing devices (including Kepler, one of the fastest and most efficient high performance computing technology) with various operating systems. The full implementation of the code is included.     

GPU-based anisotropic diffusion algorithm for video image denoising

Image filtering is the process of removing noise which perturbs image analysis methods. In some applications like segmentation, denoising is intended to smooth homogeneous areas while preserving the contours. Real-time denoising is required in a lot of applications like image-guided surgical interventions, video analysis and visual serving. This paper presents an anisotropic diffusion method named the Oriented Speckle Reducing Anisotropic Diffusion (OSRAD) filter. The OSRAD works very well for denoising images with speckle noise. However, this filter has a powerful computational complexity and is not suitable for real time implementation. The purpose of this study is to decrease the processing time implementation of the OSRAD filter using a parallel processor through the optimization of the graphics processor unit. The results show that the suggested method is very effective for real-time video processing. This implementation yields a denoising video rate of 25 frames per second for 128 × 128 pixels. The proposed model magnifies the acceleration of the image filtering to 30 × compared to the standard implementation of central processing units (CPU). A quantitative comparison measure is given by parameters like the mean structural similarity index, the peak signal-to-noise ratio and the figure of merit. The modified filter is faster than the conventional OSRAD and keeps a high image quality compared to the bilateral filter and the wavelet transformation.     

Simulations of pulse propagation in optical fibers using graphics processor units

Several simple cases of pulse propagation in optical fibers have been simulated using a graphics processor unit. Comparisons with simulations in a computer processor unit are also reported. Speedups from 4 to 36 have been obtained by different numerical methods, reaching a similar accuracy both in computer processor unit and in graphics processor unit, working with double-precision floating point numbers. Best results are achieved in simulations when the number of points in t grid rises. Therefore, the results indicate that graphics processor units could be a good tool to improve numerical simulations of pulse propagation in fibers.     

GPGPU computation and visualization of three-dimensional cellular automata

This paper presents a general-purpose simulation approach integrating a set of technological developments and algorithmic methods in cellular automata (CA) domain. The approach provides a general-purpose computing on graphics processor units (GPGPU) implementation for computing and multiple rendering of any direct-neighbor three-dimensional (3D) CA. The major contributions of this paper are: the CA processing and the visualization of large 3D matrices computed in real time; the proposal of an original method to encode and transmit large CA functions to the graphics processor units in real time; and clarification of the notion of top-down and bottom-up approaches to CA that non-CA experts often confuse. Additionally a practical technique to simplify the finding of CA functions is implemented using a 3D symmetric configuration on an interactive user interface with simultaneous inside and surface visualizations. The interactive user interface allows for testing the system with different project ideas and serves as a test bed for performance evaluation. To illustrate the flexibility of the proposed method, visual outputs from diverse areas are demonstrated. Computational performance data are also provided to demonstrate the method’s efficiency. Results indicate that when large matrices are processed, computations using GPU are two to three hundred times faster than the identical algorithms using CPU.     

Biased solution of integral illumination equation via irradiance caching and path tracing on GPUs

This work presents an approach for biased photorealistic rendering on graphics processor units (GPUs). The key idea of the approach is to combine irradiance caching with coherent adaptive path tracing.     

Parallel Implementation of a Machine Learning Algorithm on GPU

The capability for understanding data passes through the ability of producing an effective and fast classification of the information in a time frame that allows to keep and preserve the value of the information itself and its potential. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. A powerful tool is provided by self-organizing maps (SOM). The goal of learning in the self-organizing map is to cause different parts of the network to respond similarly to certain input patterns. Because of its time complexity, often using this method is a critical challenge. In this paper we propose a parallel implementation for the SOM algorithm, using parallel processor architecture, as modern graphics processing units by CUDA. Experimental results show improvements in terms of execution time, with a promising speed up, compared to the CPU version and the widely used package SOM_PAK.     

Depth-of-Field Blur Effects for First-Person Navigation in Virtual Environments

Depth-of-field blur effects are well-known depth cues in human vision. Computer graphics pipelines added DOF effects early to enhance imagery realism, but real-time VR applications haven't yet introduced visual blur effects. The authors describe new techniques to improve blur rendering and report experimental results from a prototype video game implementation.