UAMD-Net: A Unified Adaptive Multimodal Neural Network for Dense Depth Completion
Author:
Affiliation:
1. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
Funder
Guangdong Provincial Key Laboratory of Human Digital Twin, China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Media Technology
Link
http://xplorestaging.ieee.org/ielx7/76/10269772/10064324.pdf?arnumber=10064324
Reference58 articles.
1. Learning Depth from Monocular Videos Using Direct Methods
2. PyTorch: An imperative style, high-performance deep learning library;paszke;Proc Adv Neural Inf Process Syst,2019
3. Deep Ordinal Regression Network for Monocular Depth Estimation
4. Semi-Supervised Monocular Depth Estimation with Left-Right Consistency Using Deep Neural Network
5. On the Uncertainty of Self-Supervised Monocular Depth Estimation
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1. Transparent Depth Completion Using Segmentation Features;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-09-09
2. Learning Pixel-Wise Continuous Depth Representation via Clustering for Depth Completion;IEEE Transactions on Circuits and Systems for Video Technology;2024-07
3. Recursive noisy label learning paradigm based on confidence measurement for semi-supervised depth completion;International Journal of Machine Learning and Cybernetics;2024-02-07
4. DySPN: Learning Dynamic Affinity for Image-guided Depth Completion;IEEE Transactions on Circuits and Systems for Video Technology;2023
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