HCC: Egocentric Depth Perception in Augmented Reality

增强现实（AR）是一种技术，其中计算机显示器将计算机生成的图形对象添加到用户对物理（真实的）世界的视图。 AR与更知名的虚拟现实（VR）不同，在VR中，观察者看到的是完全由计算机生成的图形场景。 AR使没有真实世界等价物的可视化技术成为可能;其中一种技术是X射线视觉，其中AR用户感知位于固体，不透明表面后面的物体。 在这个项目中，PI将实证研究自我中心的深度感知（从观察者到物体的距离）如何在AR中运作。 PI将进行一系列实验，观察者判断AR呈现的虚拟物体的深度。 这些实验将使用两种不同类别的依赖性测量：视觉引导动作和基于应用的任务。一种常用的视觉引导动作是盲人行走，观察者看到目标，遮住眼睛，然后在没有视觉的情况下走到目标位置。 有很好的理论论据表明，视觉引导的行动测量了一个相对纯粹的自我中心距离，不受观察者的认知知识污染。 此外，存在大量的经验数据，其描述了现实世界对象和用VR显示设备观看的虚拟对象两者的视觉定向动作距离判断。 这些数据表明，在全线索条件下，真实世界的物体在20米范围内没有系统误差，而VR物体的距离被系统性地低估了（这一现象已被广泛研究，但尚未得到充分解释）。基于应用的任务受到AR技术引人注目的应用的激励。其中一个任务是感知匹配，其中虚拟和真实的对象的深度是匹配的;该任务是AR在医学和图像辅助手术中的应用的重要组成部分，城市环境和建筑物中的AR态势感知等。 另一个这样的任务是强制选择，其中虚拟对象的深度被放置到相对于其他对象的少量类别中的一个。 这项任务的动机是应用程序，如AR机场控制塔和AR城市态势感知系统，观察者必须作出决定的基础上的总体空间布局的虚拟和真实的objects.Although自我中心的深度感知的现实世界的对象和VR呈现的虚拟对象已经被广泛研究，目前存在的问题很少的经验数据，缺乏这项研究将纠正。 此外，由于目前的研究将使用两个免费的依赖措施的类别，他们将允许测量的程度，如VR低估效应，这已被发现的视觉定向动作任务的现象，也存在于定性不同的依赖措施。 这将有助于解决有争议的问题，在何种程度上产生这种现象的选择依赖的措施与更深层次的感知mechanism.Broader影响：在应用背景下，更好地了解AR深度感知如何运作是必要的许多令人信服的AR应用程序被实现，通过这项活动收集的经验数据将加速AR应用程序的开发。 此外，通过这项活动，一系列学生将获得计算机图形学和人类主体经验方法的融合;毕业后，这些学生将能够为计算机图形学中应用感知的重要新兴研究领域做出贡献。

Augmented reality (AR) is a technology where computer displays add (superimpose) computer-generated, graphical objects to a user's view of the physical (real) world. AR is distinguished from the better-known virtual reality (VR), wherein an observer sees an entirely computer-generated graphical scene. AR makes possible visualization techniques that have no real-world equivalent; one such technique is x-ray vision, where AR users perceive objects which are located behind solid, opaque surfaces. In this project the PI will empirically study how egocentric depth perception (the distance from an observer to an object) operates in AR. The PI will conduct a series of experiments, in which observers judge the depth of AR-presented virtual objects. These experiments will use two different categories of dependent measures: visually directed actions, and application-based tasks.A commonly used visually directed action is blind walking, where observers view a target, cover their eyes, and then walk to the target location without sight. There are good theoretical arguments that visually directed actions measure a relatively pure percept of egocentric distance, uncontaminated by observers' cognitive knowledge. Furthermore, there is a substantial body of empirical data that describes visually directed action distance judgments of both real-world objects, and virtual objects viewed with VR display devices. These data indicate that, under full-cue conditions, real-world objects are judged without systematic error up to ~20 meters, while the distance of VR objects is systematically underestimated (a phenomenon which has been studied extensively but not yet fully explained).The application-based tasks are motivated by compelling applications of AR technology. One such task is perceptual matching, where the depth of a virtual and a real object are matched; this task is an important component of AR applications in medicine and image-assisted surgery, AR situation awareness in urban settings and buildings, and others. Another such task is forced choice, where the depth of a virtual object is placed into one of a small number of categories relative to other objects. This task is motivated by applications such as an AR airport control tower and an AR urban situation awareness system, where observers must make decisions based on the gross spatial arrangement of virtual and real objects.Although the egocentric depth perception of real-world objects and VR-presented virtual objects has been widely studied, currently there exists very little empirical data on the issue, an absence this research will correct. Furthermore, because the present studies will use two complimentary categories of dependent measures, they will allow measuring the degree to which phenomena such as the VR underestimation effect, which has been found by visually directed action tasks, is also present in qualitatively different dependent measures. This will help resolve controversial questions regarding the degree to which such phenomena arise from the choice of dependent measure versus deeper perceptual mechanisms.Broader Impacts: In an applied context, a better understanding of how AR depth perception operates is necessary for many compelling AR applications to be realized, and the empirical data gathered through this activity will hasten AR application development. In addition, through this activity a series of students will receive a blend of experience in both computer graphics and human-subject empirical methods; upon graduation these students will be well-positioned to contribute to the important emerging research area of applied perception in computer graphics.