MFF-Net: Towards Efficient Monocular Depth Completion With Multi-Modal Feature Fusion-Reference-Cited by-同舟云学术

MFF-Net: Towards Efficient Monocular Depth Completion With Multi-Modal Feature Fusion

Published:2023-02 Issue:2 Volume:8 Page:920-927
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Liu Lina¹^ORCID,Song Xibin²^ORCID,Sun Jiadai³^ORCID,Lyu Xiaoyang¹,Li Lin¹^ORCID,Liu Yong¹^ORCID,Zhang Liangjun²^ORCID

Affiliation:

1. State Key Laboratory of Industrial Control Technology, Institute of Cyber-Systems and Control, Zhejiang University, Hangzhou, China

2. Robotics and Autonomous Driving Lab, Baidu Research, Beijing, China

3. School of Electronics and Information, Northwestern Polytechnical University, Xi'An, China

Funder

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9990971/10008014.pdf?arnumber=10008014

Reference42 articles.

1. Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End

2. PENet: Towards Precise and Efficient Image Guided Depth Completion

3. SemAttNet: Toward Attention-Based Semantic Aware Guided Depth Completion

4. Indoor Segmentation and Support Inference from RGBD Images

5. DeepLiDAR: Deep Surface Normal Guided Depth Prediction for Outdoor Scene From Sparse LiDAR Data and Single Color Image

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