A Review of Deep Learning-Based Semantic Segmentation for Point Cloud
In recent years, the popularity of depth sensors and 3D scanners has led to a rapid development of 3D point clouds. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. Recent advances in this topic are dominantly led by de...
Main Authors: | , , , |
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Format: | Article |
Language: | English |
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IEEE
2019-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/8930503/ |
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author | Jiaying Zhang Xiaoli Zhao Zheng Chen Zhejun Lu |
author_facet | Jiaying Zhang Xiaoli Zhao Zheng Chen Zhejun Lu |
author_sort | Jiaying Zhang |
collection | DOAJ |
description | In recent years, the popularity of depth sensors and 3D scanners has led to a rapid development of 3D point clouds. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. Recent advances in this topic are dominantly led by deep learning-based methods. In this paper, we provide a survey covering various aspects ranging from indirect segmentation to direct segmentation. Firstly, we review methods of indirect segmentation based on multi-views and voxel grids, as well as direct segmentation methods from different perspectives including point ordering, multi-scale, feature fusion and fusion of graph convolutional neural network (GCNN). Then, the common datasets for point cloud segmentation are exposed to help researchers choose which one is the most suitable for their tasks. Following that, we devote a part of the paper to analyze the quantitative results of these methods. Finally, the development trend of point cloud semantic segmentation technology is prospected. |
first_indexed | 2024-12-19T22:36:28Z |
format | Article |
id | doaj.art-2a6ec79d68f24500875b946f590c8550 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-19T22:36:28Z |
publishDate | 2019-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-2a6ec79d68f24500875b946f590c85502022-12-21T20:03:11ZengIEEEIEEE Access2169-35362019-01-01717911817913310.1109/ACCESS.2019.29586718930503A Review of Deep Learning-Based Semantic Segmentation for Point CloudJiaying Zhang0https://orcid.org/0000-0002-0465-6176Xiaoli Zhao1https://orcid.org/0000-0002-5859-3211Zheng Chen2https://orcid.org/0000-0001-7192-900XZhejun Lu3https://orcid.org/0000-0001-6617-4124School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, ChinaSchool of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, ChinaIn recent years, the popularity of depth sensors and 3D scanners has led to a rapid development of 3D point clouds. Semantic segmentation of point cloud, as a key step in understanding 3D scenes, has attracted extensive attention of researchers. Recent advances in this topic are dominantly led by deep learning-based methods. In this paper, we provide a survey covering various aspects ranging from indirect segmentation to direct segmentation. Firstly, we review methods of indirect segmentation based on multi-views and voxel grids, as well as direct segmentation methods from different perspectives including point ordering, multi-scale, feature fusion and fusion of graph convolutional neural network (GCNN). Then, the common datasets for point cloud segmentation are exposed to help researchers choose which one is the most suitable for their tasks. Following that, we devote a part of the paper to analyze the quantitative results of these methods. Finally, the development trend of point cloud semantic segmentation technology is prospected.https://ieeexplore.ieee.org/document/8930503/3D point cloudsdeep learningfeature fusiongraph convolutional neural networksemantic segmentation |
spellingShingle | Jiaying Zhang Xiaoli Zhao Zheng Chen Zhejun Lu A Review of Deep Learning-Based Semantic Segmentation for Point Cloud IEEE Access 3D point clouds deep learning feature fusion graph convolutional neural network semantic segmentation |
title | A Review of Deep Learning-Based Semantic Segmentation for Point Cloud |
title_full | A Review of Deep Learning-Based Semantic Segmentation for Point Cloud |
title_fullStr | A Review of Deep Learning-Based Semantic Segmentation for Point Cloud |
title_full_unstemmed | A Review of Deep Learning-Based Semantic Segmentation for Point Cloud |
title_short | A Review of Deep Learning-Based Semantic Segmentation for Point Cloud |
title_sort | review of deep learning based semantic segmentation for point cloud |
topic | 3D point clouds deep learning feature fusion graph convolutional neural network semantic segmentation |
url | https://ieeexplore.ieee.org/document/8930503/ |
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