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

Author:

Wang Zhipeng1ORCID,Geng Yixuan1ORCID,Jia Limin1ORCID,Qin Yong1ORCID,Chai Yuanyuan2,Tong Lei1ORCID,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

Reference46 articles.

1. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

2. Classification of point cloud for road scene understanding with multiscale voxel deep network;roynard;Proc 10th Work Planning Perceptionand Navig Intell Veh (PPNIV),2018

3. An Optimized Deep Neural Network for Overhead Contact System Recognition from LiDAR Point Clouds

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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