TY - GEN
T1 - 360ViewPET
T2 - 23rd IEEE International Symposium on Multimedia, ISM 2021
AU - Zhou, Qian
AU - Chen, Bo
AU - Yang, Zhe
AU - Guo, Hongpeng
AU - Nahrstedt, Klara
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Immersive virtual tours based on 360-degree cameras, showing famous outdoor scenery, are becoming more and more desirable due to travel costs, pandemics and other constraints. To feel immersive, a user must receive the view accurately corresponding to her position and orientation in the virtual space when she moves inside, and this requires cameras' orientations to be known. Outdoor tour contexts have numerous, ultra-sparse cameras deployed across a wide area, making camera pose estimation challenging. As a result, pose estimation techniques like SLAM, which require mobile or dense cameras, are not applicable. In this paper we present a novel strategy called 360ViewPET, which automatically estimates the relative poses of two stationary, ultra-sparse (15 meters apart) 360-degree cameras using one equirectangular image taken by each camera. Our experiments show that it achieves accurate pose estimation, with a mean error as low as 0.9 degree.
AB - Immersive virtual tours based on 360-degree cameras, showing famous outdoor scenery, are becoming more and more desirable due to travel costs, pandemics and other constraints. To feel immersive, a user must receive the view accurately corresponding to her position and orientation in the virtual space when she moves inside, and this requires cameras' orientations to be known. Outdoor tour contexts have numerous, ultra-sparse cameras deployed across a wide area, making camera pose estimation challenging. As a result, pose estimation techniques like SLAM, which require mobile or dense cameras, are not applicable. In this paper we present a novel strategy called 360ViewPET, which automatically estimates the relative poses of two stationary, ultra-sparse (15 meters apart) 360-degree cameras using one equirectangular image taken by each camera. Our experiments show that it achieves accurate pose estimation, with a mean error as low as 0.9 degree.
KW - 360 camera
KW - pose estimation
KW - virtual tourism
UR - http://www.scopus.com/inward/record.url?scp=85125010686&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125010686&partnerID=8YFLogxK
U2 - 10.1109/ISM52913.2021.00008
DO - 10.1109/ISM52913.2021.00008
M3 - Conference contribution
AN - SCOPUS:85125010686
T3 - Proceedings - 23rd IEEE International Symposium on Multimedia, ISM 2021
SP - 1
EP - 8
BT - Proceedings - 23rd IEEE International Symposium on Multimedia, ISM 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 November 2021 through 1 December 2021
ER -