TY - GEN
T1 - Delay Compensation for Actuated Stereoscopic 360 Degree Telepresence Systems with Probabilistic Head Motion Prediction
AU - Aykut, Tamay
AU - Burgmair, Christoph
AU - Karimi, Mojtaba
AU - Xu, Jingyi
AU - Steinbach, Eckehard
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/3
Y1 - 2018/5/3
N2 - Communication delay is a major challenge for the acceptance of telepresence applications. It is particularly critical when the user experiences the remote environment via a Head-Mounted-Display. The lag between head motion and display response results in motion sickness, indisposition, and, at worst, abortion of the telepresence session. In this paper, we propose a delay compensation approach for 3D 360° telepresence systems realized with a mechanically actuated stereoscopic vision system. We further introduce a novel metric to evaluate the achievable level of delay compensation. We investigate state-of-the-art head motion predictors and propose a novel probabilistic prediction paradigm, which can half the mean prediction error and improve the level of delay compensation by up to 26%. The general validity of our approach is shown by means of two independent real head motion datasets. The experimental results verify that average compensation rates of more than 99% can be achieved for communication delays between 100-500ms.
AB - Communication delay is a major challenge for the acceptance of telepresence applications. It is particularly critical when the user experiences the remote environment via a Head-Mounted-Display. The lag between head motion and display response results in motion sickness, indisposition, and, at worst, abortion of the telepresence session. In this paper, we propose a delay compensation approach for 3D 360° telepresence systems realized with a mechanically actuated stereoscopic vision system. We further introduce a novel metric to evaluate the achievable level of delay compensation. We investigate state-of-the-art head motion predictors and propose a novel probabilistic prediction paradigm, which can half the mean prediction error and improve the level of delay compensation by up to 26%. The general validity of our approach is shown by means of two independent real head motion datasets. The experimental results verify that average compensation rates of more than 99% can be achieved for communication delays between 100-500ms.
UR - https://www.scopus.com/pages/publications/85050991759
U2 - 10.1109/WACV.2018.00222
DO - 10.1109/WACV.2018.00222
M3 - Conference contribution
AN - SCOPUS:85050991759
T3 - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
SP - 2010
EP - 2018
BT - Proceedings - 2018 IEEE Winter Conference on Applications of Computer Vision, WACV 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 18th IEEE Winter Conference on Applications of Computer Vision, WACV 2018
Y2 - 12 March 2018 through 15 March 2018
ER -