Semantically guided self‐supervised monocular depth estimation
Abstract Depth information plays an important role in the vision‐related activities of robots and autonomous vehicles. An effective method to obtain 3D scene information is self‐supervised monocular depth estimation, which utilizes large and diverse monocular video datasets during the training proce...
Main Authors: | Xiao Lu, Haoran Sun, Xiuling Wang, Zhiguo Zhang, Haixia Wang |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2022-04-01
|
Series: | IET Image Processing |
Online Access: | https://doi.org/10.1049/ipr2.12409 |
Similar Items
-
Self-Supervised Monocular Depth Estimation With Extensive Pretraining
by: Hyukdoo Choi
Published: (2021-01-01) -
Self‐supervised monocular depth estimation via asymmetric convolution block
by: Lingling Hu, et al.
Published: (2022-06-01) -
Self-Supervised Monocular Depth Estimation Based on Channel Attention
by: Bo Tao, et al.
Published: (2022-06-01) -
Joint Soft–Hard Attention for Self-Supervised Monocular Depth Estimation
by: Chao Fan, et al.
Published: (2021-10-01) -
Monocular Depth Estimation with Self-Supervised Learning for Vineyard Unmanned Agricultural Vehicle
by: Xue-Zhi Cui, et al.
Published: (2022-01-01)