Dynamic Object Classification of Low-Resolution Point Clouds: An LSTM-Based Ensemble Learning Approach-Reference-Cited by-同舟云学术

Dynamic Object Classification of Low-Resolution Point Clouds: An LSTM-Based Ensemble Learning Approach

Published:2023-12 Issue:12 Volume:8 Page:8255-8262
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Zhang Shaoming¹^ORCID,Yao Tangjun¹^ORCID,Wang Jianmei¹^ORCID,Feng Tiantian¹^ORCID,Wang Zhong²^ORCID

Affiliation:

1. College of Surveying and Geo-Informatics, Tongji University, Shanghai, China

2. College of Software Engineering, Tongji University, Shanghai, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

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/ielx7/7083369/10287676/10286119.pdf?arnumber=10286119

Reference40 articles.

1. LP-3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks

2. LWSIS: LiDAR-Guided Weakly Supervised Instance Segmentation for Autonomous Driving

3. 3DmFV: Three-Dimensional Point Cloud Classification in Real-Time Using Convolutional Neural Networks

4. ProposalContrast: Unsupervised Pre-training for LiDAR-Based 3D Object Detection

5. Multi-view Convolutional Neural Networks for 3D Shape Recognition

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