TY - GEN
T1 - GlobalDepth
T2 - 56th IEEE International Symposium on Circuits and Systems, ISCAS 2023
AU - Yu, Huimin
AU - Li, Ruoqi
AU - Xiao, Zhuoling
AU - Yan, Bo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Monocular depth estimation is a significant task in computer vision, which can be widely used in Simultaneous Localization and Mapping (SLAM) and navigation. However, the current unsupervised approaches have limitations in global information perception, especially at distant objects and the boundaries of the objects. To overcome this weakness, we propose a global-aware attention model called GlobalDepth for depth estimation, which includes two essential modules: Global Feature Extraction (GFE) and Selective Feature Fusion (SFF). GFE considers the correlation among multiple channels and refines the encoder feature by extending the receptive field of the network. Furthermore, we restructure the skip connection by employing SFF between the low-level and the high-level features in element wise, rather than simply concatenation or addition at the feature level. Our model excavates the key information and enhances the ability of global perception to predict details of the scene. Extensive experimental results demonstrate that our method reduces the absolute relative error by 10.32% compared with other state-of-the-art models on KITTI datasets.
AB - Monocular depth estimation is a significant task in computer vision, which can be widely used in Simultaneous Localization and Mapping (SLAM) and navigation. However, the current unsupervised approaches have limitations in global information perception, especially at distant objects and the boundaries of the objects. To overcome this weakness, we propose a global-aware attention model called GlobalDepth for depth estimation, which includes two essential modules: Global Feature Extraction (GFE) and Selective Feature Fusion (SFF). GFE considers the correlation among multiple channels and refines the encoder feature by extending the receptive field of the network. Furthermore, we restructure the skip connection by employing SFF between the low-level and the high-level features in element wise, rather than simply concatenation or addition at the feature level. Our model excavates the key information and enhances the ability of global perception to predict details of the scene. Extensive experimental results demonstrate that our method reduces the absolute relative error by 10.32% compared with other state-of-the-art models on KITTI datasets.
KW - global feature extraction
KW - selective feature fusion
KW - unsupervised monocular depth estiamtion
UR - http://www.scopus.com/inward/record.url?scp=85167732625&partnerID=8YFLogxK
U2 - 10.1109/ISCAS46773.2023.10181346
DO - 10.1109/ISCAS46773.2023.10181346
M3 - Conference contribution
AN - SCOPUS:85167732625
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - ISCAS 2023 - 56th IEEE International Symposium on Circuits and Systems, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 21 May 2023 through 25 May 2023
ER -