@inproceedings{591d15aa2508468cacb9986784c7d9e2,
title = "Deep 6-DOF Head Motion Prediction for Latency in Lightweight Augmented Reality Glasses",
abstract = "Computationally expensive rendering of virtual 3D objects in mixed reality applications of lightweight AR glasses can be performed by a remotely connected external server. However, nonnegligible 6DOF pose error caused by the remote rendering latency results in 3D visual inconsistency which can be hardly removed by 2D image correction using IMU. In this paper, we propose a novel 6DOF pose prediction algorithm based on learnable combination of consistent motion model and deep prediction. We formulate the combination of both as controlled residual learning and model ensemble. We build a dataset and demonstrate that our algorithm provides accurate prediction under 200ms. ",
keywords = "6-DOF pose prediction, AR glasses, augmented reality, deep learning, mixed reality",
author = "Seongwook Yoon and Lim, {Hee Jeong} and Kim, {Jae Hyun} and Lee, {Hong Seok} and Kim, {Yun Tae} and Sanghoon Sull",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics, ICCE 2022 ; Conference date: 07-01-2022 Through 09-01-2022",
year = "2022",
doi = "10.1109/ICCE53296.2022.9730327",
language = "English",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE International Conference on Consumer Electronics, ICCE 2022",
}