HCC: Egocentric Depth Perception in Augmented Reality

增强现实(AR)是一种计算机显示器将计算机生成的图形对象添加(叠加)到用户对物理(真实)世界的看法的技术。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使可视化技术成为可能，而这些技术在现实世界中是没有等价物的；其中一种技术是X射线视觉，AR用户可以感知到位于固体、不透明表面后面的物体。在这个项目中，PI将经验性地研究自我中心深度知觉(从观察者到对象的距离)在AR中是如何运作的。PI将进行一系列实验，观察者在这些实验中判断AR呈现的虚拟对象的深度。这些实验将使用两种不同类别的依赖测量：视觉定向动作和基于应用程序的任务。通常使用的视觉定向动作是盲人行走，即观察者观看目标，遮住眼睛，然后步行到目标位置，而看不见。有很好的理论论证认为，视觉引导的行为衡量的是相对纯粹的自我中心距离，不受观察者认知知识的影响。此外，存在大量的经验数据，其描述真实世界对象和利用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，在全提示条件下，对真实世界对象的判断没有系统误差，最长可达~20米，而VR对象的距离被系统性低估(这一现象已被广泛研究，但尚未完全解释)。基于应用的任务是由AR技术的强制性应用所推动的。其中一个这样的任务是感知匹配，即虚拟对象和真实对象的深度匹配；该任务是AR在医学和图像辅助手术中的应用、城市环境和建筑中的AR情况感知等的重要组成部分。另一个这样的任务是强迫选择，即虚拟物体的深度被归入相对于其他物体的少数类别中的一个。这项任务是由AR机场控制塔和AR城市状况感知系统等应用程序推动的，在这些应用程序中，观察者必须根据虚拟和真实对象的总体空间布局做出决策。尽管对现实世界对象和虚拟现实呈现的虚拟对象的自我中心深度知觉已经进行了广泛的研究，但目前关于这一问题的经验数据非常少，这一研究将纠正这一缺失。此外，由于本研究将使用两类互补的依赖测量，它们将允许测量视觉定向动作任务发现的VR低估效应等现象在多大程度上也存在于不同的依赖测量中。这将有助于解决有争议的问题，即这种现象在多大程度上是由于选择依赖度量还是选择更深层次的感知机制而产生的。广泛的影响：在应用环境中，更好地理解AR深度感知是如何运作的，对于实现许多令人信服的AR应用是必要的，通过这一活动收集的经验数据将加速AR应用的开发。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学科经验方法的混合经验；毕业后，这些学生将处于有利地位，为计算机图形学中应用感知这一重要的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.