A Survey of Calibration Methods for Optical See-Through Head-Mounted Displays

TL;DR

This survey comprehensively reviews calibration methods for optical see-through head-mounted displays, highlighting techniques, evaluation metrics, and future research opportunities in aligning virtual and physical environments.

Contribution

It provides the first detailed overview of calibration procedures for OST HMDs, including manual and automatic methods, and discusses evaluation metrics and future research directions.

Findings

Various calibration techniques are categorized and compared.

Evaluation metrics for calibration accuracy are summarized.

Opportunities for improving calibration methods are identified.

Abstract

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. % relative to the tracking coordinate system, and, hence, its position in 3D space.