TY - GEN
T1 - Real-time Depth Estimation for Aerial Panoramas in Virtual Reality
AU - Xu, Di
AU - Liu, Xiaojun
AU - Zhang, Yanning
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/3
Y1 - 2020/3
N2 - With the emergence of consumer-level 360° panoramic cameras, omnidirectional RGB images and videos are now easy to be captured either in the hand-held mode or from a drone. Although previous works achieve plausible results in room-sized panoramic datasets, they are limited in indoor scenes with little rotations. In this paper, we present a real-time depth estimation method for more challenging aerial panoramas, where the viewing angle is changing rapidly and the lighting condition is more complicated. Our graph convolutional network(GCN)-based framework makes full use of the global connection information of the omnidirectional images, and is trained with extensive outdoor data. Experiments show that our method is robust to estimate the depth of outdoor aerial panoramas captured from various angles accurately.
AB - With the emergence of consumer-level 360° panoramic cameras, omnidirectional RGB images and videos are now easy to be captured either in the hand-held mode or from a drone. Although previous works achieve plausible results in room-sized panoramic datasets, they are limited in indoor scenes with little rotations. In this paper, we present a real-time depth estimation method for more challenging aerial panoramas, where the viewing angle is changing rapidly and the lighting condition is more complicated. Our graph convolutional network(GCN)-based framework makes full use of the global connection information of the omnidirectional images, and is trained with extensive outdoor data. Experiments show that our method is robust to estimate the depth of outdoor aerial panoramas captured from various angles accurately.
KW - Computer Graphics
KW - Computing methodologies
KW - Graphics systems and interface
KW - Virtual reality
UR - http://www.scopus.com/inward/record.url?scp=85085392220&partnerID=8YFLogxK
U2 - 10.1109/VRW50115.2020.00203
DO - 10.1109/VRW50115.2020.00203
M3 - 会议稿件
AN - SCOPUS:85085392220
T3 - Proceedings - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020
SP - 705
EP - 706
BT - Proceedings - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Conference on Virtual Reality and 3D User Interfaces, VRW 2020
Y2 - 22 March 2020 through 26 March 2020
ER -