云环境下面向“虚实融合”网络的SDN构建方法

在网络仿真实验场景的构建中,复杂实体终端难以实现虚拟仿真,需要对实体终端进行接入,但现有接入方式部署复杂、网络性能存在瓶颈、不能适用于大规模网络场景。为有效解决上述问题,提出了一种基于SDN(softwaredefined-network)的虚实融合网络仿真构建方法,利用SDN控制器结合流表构造算法实现对虚实网络的链路管理和数据连通,并设计开发了一套用于虚实融合网络仿真的原型系统,通过SDN控制器实现云平台中虚拟实例与云外实体终端共同组网,构建虚实融合的网络仿真实验场景。利用该原型系统,实验证明了基于SDN的网络仿真方法可以实现云平台中虚拟实例与实体终端高效大规模组网,且具有良好的网络性能。     

基于OpenGL ES的移动增强现实虚实融合

传统移动增强现实系统中虚拟模型和场景之间存在明显的视觉差异,不能够给予用户良好、逼真的真实体验在基于移动增强现实系统中使用OpenGL ES技术来实现高逼真度的虚实场景融合,研究内容主要包括三维虚拟模型的解析、渲染及管理和虚实融合场景图层设计的关系,最终完成高渲染性、高融合度的虚实融合场景的构建.实验结果表明,在OpenGL ES环境下,可以将三维虚拟物体注册到真实场景中,达到了虚实融合的效果,为用户展示出高沉浸度、无缝的视觉体验.     

基于图像分解的光照估计算法

增强现实技术的目的在于将计算机生成的虚拟物体叠加到真实场景中。实现良好的虚实融合需要对场景光照进行估算,针对高光场景,利用场景中的不同反射光信息对场景进行有效的光照估计,首先通过基于像素聚类方法的图像分解对图像进行反射光的分解,得到漫反射图和镜面反射图,对漫反射图进行进一步的本征图像分解,得到反照率图和阴影图;之后结合分解结果和场景深度对输入图像的光照信息进行计算;最后使用全局光照模型对虚拟物体进行渲染,可以得到虚实场景高度融合的光照效果。     

基于多视频的虚实融合系统

提出了一种基于多视频的虚实融合可视化系统的构建方法,旨在将真实世界中的图像和视频融合到虚拟场景中,用视频图像中的纹理和动态信息去丰富虚拟场景,提高虚拟环境的真实性,得到一种增强的虚拟环境.利用无人机采集图像来重建虚拟场景,并借助图像特征点的匹配来实现视频图像的注册.然后利用投影纹理映射技术,将图像投影到虚拟场景中.视频中的动态物体由于在虚拟环境中缺失对应的三维模型,直接投影,当视点发生变化时会产生畸变.首先检测和追踪这些物体,然后尝试使用多种显示方式来解决畸变问题.此外,系统还考虑有重叠区域的多视频之间的融合.实验结果表明,所构造的虚实融合环境是十分有益的.     

基于环境映照自动对齐的高质量虚实融合技术

要实现高品质的增强现实效果需要解决虚拟物体与现实场景的光照一致性问题.虽然采用HDR技术能获取场景的环境映照,但需要解决所获取的光照环境信息与真实场景的对齐问题.为此提出一种基于特征自动匹配的环境映照对齐方法.首先采用Affine-SIFT算法和随机抽样一致性算法对环境映照和拍摄场景进行特征匹配并优化匹配结果,然后利用基于运动推断结构的摄像机定标算法求得匹配对的三维位置,从而计算出环境映照与真实场景的对应关系,实现了二者的自动对齐.基于该技术搭建的高真实感实时虚实融合系统采用基于关键帧的相机跟踪技术,可以实时地将虚拟物体注册到拍摄的视频场景里,并允许对其进行实时的交互编辑.在渲染时有效地利用了自动对齐后的光照环境信息,采用重要性采样算法和阴影映射技术实现了实时的高质量渲染.实验结果表明,所搭建的增强现实系统很好地解决了实时虚实融合中的几何一致性和光照一致性问题.     

虚实融合场景中的深度感知研究综述

混合现实系统可以提供虚拟信息和真实环境实时叠加的虚实融合场景,在教育培训、文物保护、军事仿真、装备制造、手术医疗和展览展示等领域具有十分广阔的应用前景。混合现实系统首先利用标定数据构建虚拟摄像机模型,然后根据头部跟踪结果和虚拟摄像机位置实时绘制虚拟内容并将其叠加在真实环境中,用户通过虚实融合场景中渲染的图形化线索和虚拟物体特征感知其深度信息,但存在用于指导虚实融合场景绘制的视觉规律和感知理论匮乏、图形化线索可提供的绝对深度信息缺失和虚拟物体的渲染维度和特征指标不足等问题。本文分析了面向虚实融合场景绘制渲染的视觉规律,从用户感知的角度出发,围绕虚实融合场景中图形化线索绘制和虚拟物体渲染等展开综述,并对虚实融合场景中深度感知的研究趋势和重点进行展望和预测。     

虚实结合虚拟战场场景生成技术研究

阐述了虚实结合构建虚拟战场场景的基本思想,将虚景和实景相结合,对于需要操纵和改变外观轮廓形状的目标视景采用虚景即基于几何模型的方法建模,对于环境视景采用实景即基于图像（IBR）的方法建模,并对这两种模型进行图像合成,实现的场景既具有很好的真实感,又具有很好的实时性和可操纵性。实现了一个图像和几何模型相结合的虚拟战场视景系统,介绍了系统的结构、目标视景三维模型生成、环境视景的生成及虚拟场景图像生成与显示等关键技术。     

增强现实系统中的虚拟交互方法

在基于ARToolKit的增强现实系统中,注册在不同标识物上的三维虚拟物体因交互行为而发生碰撞时,虚实融合会失真。为解决该问题,提出一种能增加虚拟交互功能的碰撞反应算法。根据检测到的碰撞信息,实时调整虚拟物体碰撞后相对于标识物的位置和姿态,消除物体穿透现象,保持虚实融合场景的真实感。实验结果表明,该虚拟交互方法能获得比较自然和真实的交互性体验。     

基于Kinect的民族弓弦乐器虚拟演奏系统

利用Kinect相机结合增强现实技术和手势识别方法设计并实现了一个弓弦乐器虚拟演奏系统——以二胡为例.将Kinect获取的现实场景和虚拟乐器融合在一起绘制成增强现实场景.通过Kinect得到的深度数据和贝叶斯肤色模型将用户的左手分割出来,并再次绘制在增强图像上形成新的图像,从而解决虚拟演奏场景中的虚实遮挡问题.利用基于反向动力学和马尔可夫模型的三维虚拟手势拟合方法,对演奏过程中的左手手势进行识别,并结合右手的运动状态完成乐器的虚拟演奏.     

利用深度图像的虚实物体碰撞交互研究与实现

对传统增强现实系统中虚拟物体与真实物体难以进行碰撞交互的问题,提出一种对深度图像中的场景进行分割,并基于分割结果构建代理几何体的方法来实现虚、实物体的碰撞交互。采用Kinect等深度获取设备获取当前真实场景的彩色图像信息和深度图像信息;通过深度图像的法向聚类及平面拟合技术来识别出场景中的主平面区域;对除去主平面区域的其他聚类点云区域进行融合处理,得到场景中的其他主要物体区域;为识别出的主平面构建虚拟平面作为该平面的代理几何体,为分割出的物体构建包围盒来作为其代理几何体。将这些代理几何体叠加到真实物体上,并对之赋予物理属性,即可模拟实现虚拟物体与真实物体的碰撞交互。实验结果表明,该方法可有效分割简单场景,从而实现虚实交互。     

Assessing future landscapes using enhanced mixed reality with semantic segmentation by deep learning

Architecture, engineering, and construction projects need to be promoted in harmony with the natural environment and with the aim of preserving people’s living environment. At the planning and design stage, decision-makers and stakeholders share and assess landscape images during and after construction in order to avoid as much uncertainty as possible when performing environmental impact assessment. Given the lack of a standard visualization method for future landscapes that do not yet exist, mixed reality (MR), which overlays virtual content onto a real scene, has attracted attention in the field of landscape design. One challenge in MR is occlusion, which occurs when virtual objects obscure physical objects that should be rendered in the foreground. In MR-based landscape visualization, the distance between the MR camera and real objects located in front of the virtual objects might vary and might be large, causing difficulty for existing occlusion handling methods. In the process of landscape design, an evidence-based approach has also become important. Landscape index estimation using semantic segmentation by deep learning, which can recognize the surrounding environment, has been actively studied for landscape assessment. In this study, semantic segmentation by deep learning was integrated into an MR system to enable dynamic occlusion handling and landscape index estimation for both existing and designed landscape assessment. This system can be operated on a mobile device with video communication over the internet by connecting to real-time semantic segmentation on a high-performance personal computer. The applicability of the developed system is demonstrated through accuracy verification and case studies.     

Exploring legibility of augmented reality X-ray

Virtual objects can be visualized inside real objects using augmented reality (AR). This visualization is called AR X-ray because it gives the impression of seeing through the real object. In standard AR, virtual information is overlaid on top of the real world. To position a virtual object inside an object, AR X-ray requires partially occluding the virtual object with visually important regions of the real object. In effect, the virtual object becomes less legible compared to when it is completely unoccluded. Legibility is an important consideration for various applications of AR X-ray. In this research, we explored legibility in two implementations of AR X-ray, namely, edge-based and saliency-based. In our first experiment, we explored on the tolerable amounts of occlusion to comfortably distinguish small virtual objects. In our second experiment, we compared edge-based and saliency-based AR X-ray methods when visualizing virtual objects inside various real objects. Moreover, we benchmarked the legibility of these two methods against alpha blending. From our experiments, we observed that users have varied preferences for proper amounts of occlusion cues for both methods. The partial occlusions generated by the edge-based and saliency-based methods need to be adjusted depending on the lighting condition and the texture complexity of the occluding object. In most cases, users identify objects faster with saliency-based AR X-ray than with edge-based AR X-ray. Insights from this research can be directly applied to the development of AR X-ray applications.     

Handling occlusions in video‐based augmented reality using depth information

Augmented Reality (AR) composes virtual objects with real scenes in a mixed environment where human–computer interaction has more semantic meanings. To seamlessly merge virtual objects with real scenes, correct occlusion handling is a significant challenge. We present an approach to separate occluded objects in multiple layers by utilizing depth, color, and neighborhood information. Scene depth is obtained by stereo cameras and two Gaussian local kernels are used to represent color, spatial smoothness. These three cues are intelligently fused in a probability framework, where the occlusion information can be safely estimated. We apply our method to handle occlusions in video‐based AR where virtual objects are simply overlapped on real scenes. Experiment results show the approach can correctly register virtual and real objects in different depth layers, and provide a spatial‐awareness interaction environment. Copyright © 2009 John Wiley & Sons, Ltd.     

高效能计算机系统虚拟化技术研究

高效能计算机对系统的性能、安全性、可靠性和易用性等方面提出了更高的要求。虚拟机技术由于具有安全性好、配置和管理灵活等特点,已广泛应用于服务整合和安全管理等领域。但是,由于虚拟机技术在性能、管理和体系结构适用性等方面原因,在高性能并行计算机系统上尚未真正实用。我们提出了一种面向高性能并行计算机的虚拟化技术:高性能虚拟计算域(HPVZ)。HPVZ技术在保证系统性能的前提下,提供了具有用户独立可定制运行环境、服务质量管理、安全隔离和动态迁移的虚拟化高性能计算环境。测试表明,HPVZ在保证用户可以获得高性能计算机的原始计算能力的基础上,方便了用户使用,并改变了高性能计算机的传统使用模式。     

VLAB中虚拟元件库建模方法研究及实现

虚拟元件库是虚拟实验室中重要的组成部分，本文将面向对象的方法应用到虚拟元件库的建模中，根据虚拟实验室系统的对象及其相互关系来构造仿真模型，在此基础上详细分析了虚拟元件库的设计和实现方法。通过虚拟元件库的创建使得虚拟元件具有良好的独立性、扩充性、可重性和可维护性，为进行虚拟实验奠定了良好的基础。     

Occlusion in collaborative augmented environments

Augmented environments superimpose computer enhancements on the real world. Such augmented environments are well suited for collaboration of multiple users. To improve the quality and consistency of the augmentation the occlusion of real objects by computer-generated objects and vice versa has to be implemented. We present methods how this can be done for a tracked user's body and other real objects and how irritating artifacts due to misalignments can be reduced. Our method is based on simulating the occlusion of virtual objects by a representation of the user modeled as kinematic chains of articulated solids. Smoothing the border between virtual world and occluding real reduces registration and modeling errors of this model. Finally, an implementation in our augmented environment and the resulting improvements are presented.     

混杂系统的虚拟对象实验室平台建设

通过对混杂系统典型实例即三水箱系统特性的分析,从系统安全的角度讨论了用于控制该系统离散状态切换序列的基于混杂自动机描述的贪婪型与周期型控制策略.依据此控制策略,利用VC＋＋与Matlab的混合编程技术开发了该混杂系统的虚拟对象实验室平台.通过对三水箱系统及仿真环境参数的设置,此平台不仅可以形象地演示虚拟对象在不同初始条件下的混杂特性,而且可以实现预定的控制策略并进行仿真研究,从而为具有此类混杂特性的混杂系统的深入研究,提供了一个虚拟的实验环境.     

层次结构辐射度渲染中的可见性测试

在辐射度渲染等整体光照模型中，可见性测试是影响整体性能的关键，为了获得较好的可见性测试效果，在光线投射方法基础上，对可见性测试方法做了如下两方面的优化，一方面引入Shaft Culling算法，作首轮筛选，以提高线投射的效率，另一方面，在可见性测试和自适应分割时，进行基于可见性的分割，以保证只有完全可见和完全不可见两种情况，这两方面优化都被引入到层次结构辐射度渲染的实现中，实验结果表明，改进过的可见性测试，不仅减少了计算量和误差，还保证了阴影边界的质量。     

大规模无线移动自组织网络混合模拟测试方法

针对已有无线移动自组织网络(MANET)测试方法存在模型简单、成本高、不易重复等问题,提出一种基于NS2的大规模MANET混合模拟测试方法(LHEN)。使用NS2的模拟功能,利用Tap代理完成虚拟分组与实际分组间的嵌套和解嵌,通过网络对象及NS2实时调度器实现虚拟环境和真实环境的通信;采用控制网络无线信号强度的方式模拟真实节点移动,完成对真实网络环境的构建。使用混合模拟和仿真两种方法分别建立大规模MANET网络进行实验对比,所测性能指标变化趋势基本一致,平均差值低于18.7%。实验结果表明,LHEN可以用于大规模MANET网络各项性能指标测试及验证。     

Relational Model for Robotic Semantic Navigation in Indoor Environments

The emergence of service robots in our environment raises the need to find systems that help the robots in the task of managing the information from human environments. A semantic model of the environment provides the robot with a representation closer to the human perception, and it improves its human-robot communication system. In addition, a semantic model will improve the capabilities of the robot to carry out high level navigation tasks. This paper presents a semantic relational model that includes conceptual and physical representation of objects and places, utilities of the objects, and semantic relation among objects and places. This model allows the robot to manage the environment and to make queries about the environment in order to do plans for navigation tasks. In addition, this model has several advantages such as conceptual simplicity and flexibility of adaptation to different environments. To test the performance of the proposed semantic model, the output for the semantic inference system is associate to the geometric and topological information of objects and places in order to do the navigation tasks.