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Proceedings Paper

Analysis of the vergence correction method in video see-through mixed reality systems
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Paper Abstract

The paper is devoted to the problem of correct vergence in the mixed reality systems. There are two main types of mixed reality devices, these are optical see-through and video see-through mixed reality systems. They differ by the way how the device user observes the real-world environment: in the optical see-through mixed reality systems the real world is observed directly and as result the vergence and accommodation conflicts are absent while in the video see-through mixed reality systems there present both vergence and accommodation conflicts. The vergence conflict is caused by the fixed orientation of the device cameras that transmit the image of the real world, and the accommodation conflict is caused by the constancy of the focal length of optical systems that form the images of the real world on the human eye retina from the CCD matrices. This paper presents the research of the possibility to restore the correct convergence of a real-world image formed by video see-through mixed reality systems. The research shows how convergence can be correctly reconstructed using images captured by fixed cameras with parallel axes located at the front side of the mixed reality headset that form images for the left and right eyes. The resulting reconstructed images are almost free from any visual artifacts. Additionally, authors show how errors in scene depth map reconstruction affects the vergence restoration quality.

Paper Details

Date Published: 18 December 2019
PDF: 10 pages
Proc. SPIE 11338, AOPC 2019: Optical Sensing and Imaging Technology, 113383Q (18 December 2019); doi: 10.1117/12.2548195
Show Author Affiliations
Andrey Zhdanov, ITMO Univ. (Russian Federation)
Yan Wang, ITMO Univ. (Russian Federation)
Dmitry Zhdanov, ITMO Univ. (Russian Federation)
Igor S. Potemin, ITMO Univ. (Russian Federation)


Published in SPIE Proceedings Vol. 11338:
AOPC 2019: Optical Sensing and Imaging Technology
John E. Greivenkamp; Jun Tanida; Yadong Jiang; HaiMei Gong; Jin Lu; Dong Liu, Editor(s)

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