Monocular depth estimation based on unsupervised learning

2019 ◽  
Author(s):  
Wan Liu ◽  
Yan Sun ◽  
XuCheng Wang ◽  
Lin Yang ◽  
Zhenrong Zheng
Keyword(s):  
Unsupervised Learning ◽  
Depth Estimation ◽  
Monocular Depth

scholarly journals An Adaptive Unsupervised Learning Framework for Monocular Depth Estimation

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 148142-148151
Author(s):  
Delong Yang ◽  
Xunyu Zhong ◽  
Lixiong Lin ◽  
Xiafu Peng
Keyword(s):  
Unsupervised Learning ◽  
Depth Estimation ◽  
Learning Framework ◽  
Monocular Depth

Unsupervised Monocular Depth Estimation for Autonomous Driving

2019 ◽  
pp. 128
Author(s):  
Chih-Shuan Huang ◽  
Wan-Nung Tsung ◽  
Wei-Jong Yang ◽  
Chin-Hsing Chen
Keyword(s):  
Depth Estimation ◽  
Autonomous Driving ◽  
Monocular Depth

scholarly journals Unsupervised Learning-Based Depth Estimation-Aided Visual SLAM Approach

2019 ◽  
Vol 39 (2) ◽  
pp. 543-570 ◽  
Author(s):  
Mingyang Geng ◽  
Suning Shang ◽  
Bo Ding ◽  
Huaimin Wang ◽  
Pengfei Zhang
Keyword(s):  
Unsupervised Learning ◽  
Depth Estimation ◽  
Visual Slam

Hierarchical Object Relationship Constrained Monocular Depth Estimation.

2021 ◽  
pp. 108116
Author(s):  
Shuai Li ◽  
Jiaying Shi ◽  
Wenfeng Song ◽  
Aimin Hao ◽  
Hong Qin
Keyword(s):  
Depth Estimation ◽  
Monocular Depth ◽  
Object Relationship

scholarly journals Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 54
Author(s):  
Peng Liu ◽  
Zonghua Zhang ◽  
Zhaozong Meng ◽  
Nan Gao
Keyword(s):  
Joint Attention ◽  
Loss Function ◽  
Depth Estimation ◽  
Depth Information ◽  
3D Vision ◽  
Network Training ◽  
Crucial Component ◽  
Benchmark Datasets ◽  
Ill Posed ◽  
Monocular Depth

Depth estimation is a crucial component in many 3D vision applications. Monocular depth estimation is gaining increasing interest due to flexible use and extremely low system requirements, but inherently ill-posed and ambiguous characteristics still cause unsatisfactory estimation results. This paper proposes a new deep convolutional neural network for monocular depth estimation. The network applies joint attention feature distillation and wavelet-based loss function to recover the depth information of a scene. Two improvements were achieved, compared with previous methods. First, we combined feature distillation and joint attention mechanisms to boost feature modulation discrimination. The network extracts hierarchical features using a progressive feature distillation and refinement strategy and aggregates features using a joint attention operation. Second, we adopted a wavelet-based loss function for network training, which improves loss function effectiveness by obtaining more structural details. The experimental results on challenging indoor and outdoor benchmark datasets verified the proposed method’s superiority compared with current state-of-the-art methods.

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