HCC: Small: Effective Augmented Reality Depth Representation Methods and Accuracy Evaluations Inspired by Medical Applications

HCC：小型：受医学应用启发的有效增强现实深度表示方法和准确性评估

• 批准号: 1320909
• 负责人: John Swan
• 金额: $ 49.82万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320909&HistoricalAwards=false
• 关键词: HCC; Small; Effective; Augmented; Reality

Augmented reality (AR) systems, which are computer systems that enhance the viewing of physical objects in the world with computer data, are currently held back from widespread use for many real-world applications because of the unsolved human-computer interaction problem of how to accurately convey to a person how far away from that person a computer-generated object is intended to appear. People using AR systems routinely misjudge the depth of AR-presented objects. This is especially true for AR objects that should appear to be located behind opaque occluding surfaces; in this case AR should produce an "x-ray vision" perceptual experience that makes the occluding surface appear to become transparent. The perceptual phenomena that underlie this problem relate to (a) conflicting depth cues that naturally arise with AR technology, especially incorrect occlusion cues in optical "x-ray vision" AR, (b) conflicting findings from techniques that have been developed to measure depth perception within reaching distance, and (c) the role of practice and feedback in training to correct these depth misjudgments.This project will evaluate AR depth representation methods and explain the underlying phenomena, with an emphasis on medical AR tasks and applications. The project will develop and evaluate a head-worn haploscope to allow researchers to study the depth cues of accommodation and vergence AR. The project will create and evaluate vergence-based methods for rendering AR information in depth; that is, techniques in which people can control the appearance of computer data inside of a physical object by rotating their eyes as is needed to look at near and far objects. The researchers on this project will collaborate with experts on the use of AR for medical applications to develop new vergence-based techniques for AR "x-ray vision" in the medical domain. Broader Impacts: Vergence-based AR applications have the potential to improve health outcomes for a broad array of medical procedures, and also to improve human capabilities in task domains such as manufacturing and equipment maintenance. This project will hasten the timeframe for successfully developing and deploying such applications. Students working on this project will be trained in an interdisciplinary context that rigorously studies the intimate interplay between computer graphics and human perception. The interdisciplinary and human-centered aspects of the project will help to recruit students who might otherwise be less likely to gravitate to computer science.

增强现实（AR）系统是利用计算机数据增强对世界中的物理对象的观看的计算机系统，由于如何向人准确地传达计算机生成的对象意图出现在离该人多远的地方的未解决的人机交互问题，目前阻碍了对许多现实世界应用的广泛使用。 使用AR系统的人通常会误判AR呈现的物体的深度。 这对于应该看起来位于不透明遮挡表面后面的AR对象尤其如此;在这种情况下，AR应该产生“X射线视觉”感知体验，使遮挡表面看起来变得透明。 作为该问题基础的感知现象涉及（a）AR技术自然产生的冲突深度线索，特别是光学“x射线视觉”AR中的不正确遮挡线索，（B）来自已经开发用于测量到达距离内的深度感知的技术的冲突发现，以及（c）练习和反馈在训练中的作用，以纠正这些深度误判。本项目将评估AR深度表示方法并解释潜在的现象，重点是医疗AR任务和应用。 该项目将开发和评估头戴式单倍镜，以使研究人员能够研究调节和聚散AR的深度线索。 该项目将创建和评估用于深度渲染AR信息的基于聚散度的方法;也就是说，人们可以通过旋转眼睛来控制物理对象内部计算机数据的外观，以查看远近物体。 该项目的研究人员将与专家合作，将AR用于医疗应用，为医疗领域的AR“x射线视觉”开发新的基于聚散度的技术。 更广泛的影响：基于Vergence的AR应用有可能改善各种医疗程序的健康结果，并提高人类在制造和设备维护等任务领域的能力。 该项目将加快成功开发和部署此类应用程序的时间表。 从事该项目的学生将在跨学科的背景下接受培训，严格研究计算机图形学和人类感知之间的密切相互作用。 该项目的跨学科和以人为本的方面将有助于招收那些不太可能被计算机科学吸引的学生。