MENet: Multi-Modal Mapping Enhancement Network for 3D Object Detection in Autonomous Driving-Reference-Cited by-同舟云学术

MENet: Multi-Modal Mapping Enhancement Network for 3D Object Detection in Autonomous Driving

Published:2024-08 Issue:8 Volume:25 Page:9397-9410
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Liu Moyun¹^ORCID,Chen Youping¹^ORCID,Xie Jingming¹^ORCID,Zhu Yijie¹,Zhang Yang²^ORCID,Yao Lei³^ORCID,Bing Zhenshan⁴^ORCID,Zhuang Genghang⁴,Huang Kai⁵^ORCID,Zhou Joey Tianyi⁶

Affiliation:

1. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

2. School of Mechanical Engineering, Hubei University of Technology, Wuhan, China

3. Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong, China

4. Department of Informatics, Technical University of Munich, Munich, Germany

5. Shenzhen Institute, and the School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

6. Institute of High Performance Computing (IHPC), and the Centre for Frontier AI Research (CFAR), Agency for Science, Technology and Research (A*STAR), Connexis Tower, Singapore

Funder

Hubei Provincial Key Research and Development Program Project of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10621861/10510171.pdf?arnumber=10510171

Reference67 articles.

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

2. TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

3. Joint 3D Proposal Generation and Object Detection from View Aggregation

4. EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection

5. Deep Continuous Fusion for Multi-sensor 3D Object Detection