PointNest: Learning Deep Multiscale Nested Feature Propagation for Semantic Segmentation of 3-D Point Clouds-Reference-Cited by-同舟云学术

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PointNest: Learning Deep Multiscale Nested Feature Propagation for Semantic Segmentation of 3-D Point Clouds

Published:2023 Issue: Volume:16 Page:9051-9066
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Wan Jie¹^ORCID,Zeng Ziyin²^ORCID,Qiu Qinjun³^ORCID,Xie Zhong⁴^ORCID,Xu Yongyang³^ORCID

Affiliation:

1. Key Laboratory of Geological and Evaluation of Ministry of Education, China University of Geosciences, Wuhan, China

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

3. School of Computer Science, Wuhan, China

4. School of Computer Science, China University of Geosciences, Wuhan, China

Funder

National Key Research and Development Program of China

Natural Science Foundation of Hubei Province of China

Open Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Atmospheric Science,Computers in Earth Sciences

Link

http://xplorestaging.ieee.org/ielx7/4609443/9973430/10251503.pdf?arnumber=10251503

Reference54 articles.

1. Navigation of a mobile robot in a dynamic environment using a point cloud map

2. A LiDAR Point Cloud Generator

3. Relevance assessment of full-waveform lidar data for urban area classification

4. Deep Learning for 3D Point Clouds: A Survey

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

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