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美国论文代写:充分沉浸式技术

美国论文代写:充分沉浸式技术。充分沉浸式技术是本文的核心重点。创造沉浸式环境背后的技术是模型和图像等的使用。为了创建虚拟设置,人体动作捕捉通常采用基于模型的方法。基于从传统生活中收集的数据绘制虚拟模型有助于创建虚拟环境。在完全沉浸的情况下,基于模型的技术通过捕获自由度绘制。与物体相关的自由度是物体在空间中移动的方式。与传统VR不同的是,动态运动能力被赋予了更多的重要性。自由度在三维空间中分为六类,这里的两类是旋转运动和平移运动。作为旋转运动的一部分,方向跟踪以俯仰、偏航和滚转测量的形式进行。平移运动跟踪是通过传统的左右移动、向前移动和向后移动等方式来完成的。接下来美国论文代写专家将为同学们讲解下有关充分沉浸式技术。

在浸入式跟踪中,可以识别出三维物体在任何时间的绝对位置。这些元素被追踪到一个人的化身上。虽然基于模型的沉浸感和技术已经得到了深入的研究,但基于图像的沉浸感技术由于其在数据映射中提供了更多的点而成为了一个比较热门的课题。更高自由度的映射和更精确的表示是可能的。像McMahan(2012)和Bailenson等人(2005)这样的研究人员认为逼真度的重要性,运动逼真度比视觉逼真度更重要。McMahan(2012)在研究工作中试图理解保真度如何在向用户呈现更复杂、更丰富的界面中发挥作用。他指出,与动作保真度相关的显示和互动保真度等元素将对用户沉浸于虚拟现实环境中的游戏策略和表现产生强烈影响。

当用户处于沉浸式环境中时,他们的存在感、参与感和适宜性的主观判断非常重要,甚至这些元素也会受到影响。现在这些争论揭示了为什么虚拟现实和沉浸式虚拟现实的未来更倾向于地图和跟踪技术的发展,比如基于图像的技术。以形象为基础的电子商务是发展的新阵地。与传统的虚拟现实环境相比,沉浸式虚拟现实设置已经利用了更复杂的映射自由度,给用户一个改进的体验。使用基于图像的模型将进一步改进。这种基于图像的模型将有更好的运动保真度相比视觉保真度,尽管挑战性一直是研究人员的焦点。该领域未来的研究重点是全息触觉技术、航空航天和国防应用(Stone, 2001)。与沉浸式技术相结合的触觉反馈系统赋予沉浸式开发的视觉图像技术自我学习的地位(Van der Meijden & Schijven, 2009;石头,2001)。可以使用更大的数据池来改进这些系统。

Fully immersive techniques are the core focus of this paper. The technique behind the creation of the immersive environment is the usage of models and images etc. Human motion capture in order to create virtual settings is usually dictated with a model-based approach. Mapping with a virtual model based on data collected from a traditional life in setting would help create the virtual settings. In the case of full immersion, the model-based technique draws by capturing degrees of freedom. The degrees of freedom in relation to the object are how the object will move in space. Unlike the traditional VR, here the dynamic motion capability is given more importance. The degrees of freedom are classified as six in number with respect to the 3D space and the two categories here are rotational movements and translational movements. Orientation tracking as part of the rotational movements is done in the form of pitch, yaw and roll measurements. Translational movement tracking is done with the traditional movements like going left and right, moving forward and backward etc.

Absolute position of the 3D object at any time will be identified in immersion tracking. These elements are tracked on a human avatar. While model-based immersion and techniques have been studied in depth, it is the image-based immersion techniques that are researched as a more current subject on account of how it provides more points in data mapping. Higher degrees of freedom in mapping and more accurate representations are possible. Researchers like McMahan (2012) and Bailenson et al. (2005) argue the importance of fidelity and that motion fidelity is more important than visual fidelity. McMahan (2012) in the research work attempts to understand how fidelity plays a role in presenting a more complex, rich interface to user. He identified that elements like display and interaction fidelity that can be associated to motion fidelity will have a strong impact on the strategy and performance of the play that the user indulges in with the VR environment.

User subjective judgment in the form of presence, engagement and their perception of suability is important when they are in immersive environment and even these elements are affected as well. Now these arguments reveal why the future of virtual reality and immersive virtual reality is more geared towards the development of techniques for mapping and tracking like image-based ones. Image-based ones are the new front to development. Compared to the traditional virtual reality environment, the immersive virtual reality settings already make use of more intricate degrees of freedom in mapping to give the user an improved experience. The use of image-based models will add to further improvement. Such image-based models will have better motion fidelity as compared against visual fidelity although challenging has been the focus of researchers. Future of research context in this area is focused on holographic haptic technology, aerospace and defence applications (Stone, 2001). Haptic feedback systems used with immersive technology have given self-learning status to the visual image technique of immersive development (Van der Meijden & Schijven, 2009; Stone, 2001). Much larger data pools are available for improving these systems.

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