Self-Attentive Local Aggregation Learning With Prototype Guided Regularization for Point Cloud Semantic Segmentation of High-Speed Railways-Reference-Cited by-同舟云学术

Self-Attentive Local Aggregation Learning With Prototype Guided Regularization for Point Cloud Semantic Segmentation of High-Speed Railways

Published:2023-10 Issue:10 Volume:24 Page:11157-11170
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Wang Zhipeng¹^ORCID,Geng Yixuan¹^ORCID,Jia Limin¹^ORCID,Qin Yong¹^ORCID,Chai Yuanyuan²,Tong Lei¹^ORCID,Liu Keyan¹

Affiliation:

1. State Key Laboratory of Rail Traffic Control and Safety and the Key Laboratory of Railway Industry of Proactive Safety and Risk Control, Beijing Jiaotong University, Beijing, China

2. JIT Research Institute, Changchun Jilin University Zhengyuan Information Technologies Company Ltd., Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Research Project of State Key Laboratory of Rail Traffic Control and Safety

Research Project of China State Railway Group Company Ltd.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/10271405/10144473.pdf?arnumber=10144473

Reference46 articles.

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4. Unstructured point cloud semantic labeling using deep segmentation networks;boulch;Proc Eurographics Workshop 3D Object Retrieval,2017

5. PointCNN: Convolution on X-transformed points;li;Proc Adv Neural Inf Process Syst,2018

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