A Survey of Calibration Methods for Optical See-Through Head-Mounted Displays
A Survey of Calibration Methods for Optical See-Through Head-Mounted Displays
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DOI：
10.1109/tvcg.2017.2754257
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发表时间：
2018-09-01
影响因子：
5.2
通讯作者：
Swan, J. Edward, II
中科院分区：
文献类型：
--
作者：
Grubert, Jens;Itoh, Yuta;Swan, J. Edward, II
光学透视头戴式显示器（OST HMD）是增强现实的主要输出介质，由于越来越多的面向消费者的模型（如Microsoft Hololens）的发布，增强现实在公众中的普及和使用率显着增长。与虚拟现实头盔不同，OST HMD本质上支持将计算机生成的图形直接添加到用户眼睛与物理世界视图之间的光路中。与大多数增强和虚拟现实系统一样，OST HMD的物理位置通常由外部或嵌入式6自由度跟踪系统确定。然而，为了适当地渲染被感知为与物理环境空间对准的虚拟对象，还需要精确地测量用户的眼睛在跟踪系统的坐标系内的位置。20多年来，研究人员提出了各种校准方法来确定所需的眼睛位置。然而，迄今为止，还没有对这些程序及其要求进行全面审查。因此，本文调查的OST HMD的校准方法的领域。具体而言，它提供了深入了解校准技术的基础知识，并概述了手动和自动方法，以及评估方法和指标。最后，它还确定了未来研究的机会。
Optical see-through head-mounted displays (OST HMDs) are a major output medium for Augmented Reality, which have seen significant growth in popularity and usage among the general public due to the growing release of consumer-oriented models, such as the Microsoft Hololens. Unlike Virtual Reality headsets, OST HMDs inherently support the addition of computer-generated graphics directly into the light path between a user's eyes and their view of the physical world. As with most Augmented and Virtual Reality systems, the physical position of an OST HMD is typically determined by an external or embedded 6-Degree-of-Freedom tracking system. However, in order to properly render virtual objects, which are perceived as spatially aligned with the physical environment, it is also necessary to accurately measure the position of the user's eyes within the tracking system's coordinate frame. For over 20 years, researchers have proposed various calibration methods to determine this needed eye position. However, to date, there has not been a comprehensive overview of these procedures and their requirements. Hence, this paper surveys the field of calibration methods for OST HMDs. Specifically, it provides insights into the fundamentals of calibration techniques, and presents an overview of both manual and automatic approaches, as well as evaluation methods and metrics. Finally, it also identifies opportunities for future research.