3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving-Reference-Cited by-同舟云学术

3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving

Published:2024-08 Issue:8 Volume:25 Page:8782-8795
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Zhuang Yuan¹,Li Qipeng¹,Chen Yiwen¹,Huai Jianzhu¹^ORCID,Li Miao²^ORCID,Ma Tianbing³^ORCID,Tang Yufei⁴^ORCID,Liang Xinlian¹

Affiliation:

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

2. Institute of Technological Sciences, Wuhan University, Wuhan, China

3. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, Anhui, China

4. Department of Computer Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL, USA

Funder

National Natural Science Foundation of China

Excellent Youth Foundation of Hubei Scientific Committee

Basic and Applied Basic Research Foundation of Guangdong Province

Special Fund of Anhui University of Science and Technology

Wuhan Artificial Intelligence Innovation Special Project

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6979/10621861/10529942.pdf?arnumber=10529942

Reference50 articles.

1. Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

2. Moving Object Segmentation in 3D LiDAR Data: A Learning-Based Approach Exploiting Sequential Data

3. Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

4. Remove, then Revert: Static Point cloud Map Construction using Multiresolution Range Images

5. RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds