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Article

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

by 1,2,3, 1,2,*, 1,2 and 1,2
1
State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
2
School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China
3
Key Laboratory of Intelligent Data Information Processing and Control of Hebei Province, Tangshan University, Tangshan 063000, China
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(1), 54; https://doi.org/10.3390/s21010054
Received: 28 November 2020 / Revised: 21 December 2020 / Accepted: 21 December 2020 / Published: 24 December 2020
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. View Full-Text
Keywords: monocular depth estimation; feature distillation; joint attention; loss function monocular depth estimation; feature distillation; joint attention; loss function
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MDPI and ACS Style

Liu, P.; Zhang, Z.; Meng, Z.; Gao, N. Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function. Sensors 2021, 21, 54. https://doi.org/10.3390/s21010054

AMA Style

Liu P, Zhang Z, Meng Z, Gao N. Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function. Sensors. 2021; 21(1):54. https://doi.org/10.3390/s21010054

Chicago/Turabian Style

Liu, Peng; Zhang, Zonghua; Meng, Zhaozong; Gao, Nan. 2021. "Monocular Depth Estimation with Joint Attention Feature Distillation and Wavelet-Based Loss Function" Sensors 21, no. 1: 54. https://doi.org/10.3390/s21010054

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