Predicting Bird's-Eye-View Semantic Representations Using Correlated Context Learning-Reference-Cited by-同舟云学术

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Predicting Bird's-Eye-View Semantic Representations Using Correlated Context Learning

Published:2024-05 Issue:5 Volume:9 Page:4718-4725
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Chen Yongquan¹^ORCID,Fan Weiming²^ORCID,Zheng Wenli³^ORCID,Huang Rui¹^ORCID,Yu Jiahui¹^ORCID

Affiliation:

1. Shenzhen Institute of Artificial Intelligence and Robotics for Society, The Chinese University of Hong Kong, Shenzhen, China

2. School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang, China

3. Dapeng Customs of the People's Republic of China, Shenzhen, China

Funder

Shenzhen Science and Technology Program

Science and Technology Program from General Administration of Customs of the People's Republic of China

Basic and Applied Basic Research Foundation of Guangdong Province

Institute of Artificial Intelligence and Robotics for Society

Zhejiang Provincial Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7083369/10474437/10487856.pdf?arnumber=10487856

Reference33 articles.

1. Understanding Bird’s-Eye View of Road Semantics Using an Onboard Camera

2. Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges

3. A comprehensive survey of LIDAR-based 3D object detection methods with deep learning for autonomous driving

4. Deep learning-based perception systems for autonomous driving: A comprehensive survey

5. Cross-View Semantic Segmentation for Sensing Surroundings

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