Point Cloud Upsampling Algorithm: A Systematic Review
Point cloud upsampling algorithms can improve the resolution of point clouds and generate dense and uniform point clouds, and are an important image processing technology. Significant progress has been made in point cloud upsampling research in recent years. This paper provides a comprehensive surve...
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Format: | Article |
Language: | English |
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MDPI AG
2022-04-01
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Series: | Algorithms |
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Online Access: | https://www.mdpi.com/1999-4893/15/4/124 |
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author | Yan Zhang Wenhan Zhao Bo Sun Ying Zhang Wen Wen |
author_facet | Yan Zhang Wenhan Zhao Bo Sun Ying Zhang Wen Wen |
author_sort | Yan Zhang |
collection | DOAJ |
description | Point cloud upsampling algorithms can improve the resolution of point clouds and generate dense and uniform point clouds, and are an important image processing technology. Significant progress has been made in point cloud upsampling research in recent years. This paper provides a comprehensive survey of point cloud upsampling algorithms. We classify existing point cloud upsampling algorithms into optimization-based methods and deep learning-based methods, and analyze the advantages and limitations of different algorithms from a modular perspective. In addition, we cover some other important issues such as public datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future research directions and open issues that should be further addressed. |
first_indexed | 2024-03-09T11:16:47Z |
format | Article |
id | doaj.art-4dff40fca9b24376a24ce19f5e966b23 |
institution | Directory Open Access Journal |
issn | 1999-4893 |
language | English |
last_indexed | 2024-03-09T11:16:47Z |
publishDate | 2022-04-01 |
publisher | MDPI AG |
record_format | Article |
series | Algorithms |
spelling | doaj.art-4dff40fca9b24376a24ce19f5e966b232023-12-01T00:28:57ZengMDPI AGAlgorithms1999-48932022-04-0115412410.3390/a15040124Point Cloud Upsampling Algorithm: A Systematic ReviewYan Zhang0Wenhan Zhao1Bo Sun2Ying Zhang3Wen Wen4Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaLogistics Engineering College, Shanghai Maritime University, Shanghai 201306, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201204, ChinaPoint cloud upsampling algorithms can improve the resolution of point clouds and generate dense and uniform point clouds, and are an important image processing technology. Significant progress has been made in point cloud upsampling research in recent years. This paper provides a comprehensive survey of point cloud upsampling algorithms. We classify existing point cloud upsampling algorithms into optimization-based methods and deep learning-based methods, and analyze the advantages and limitations of different algorithms from a modular perspective. In addition, we cover some other important issues such as public datasets and performance evaluation metrics. Finally, we conclude this survey by highlighting several future research directions and open issues that should be further addressed.https://www.mdpi.com/1999-4893/15/4/124point cloud upsamplingdeep learninggenerative adversarial network (GAN)graph convolutional network (GCN)unsupervised learning |
spellingShingle | Yan Zhang Wenhan Zhao Bo Sun Ying Zhang Wen Wen Point Cloud Upsampling Algorithm: A Systematic Review Algorithms point cloud upsampling deep learning generative adversarial network (GAN) graph convolutional network (GCN) unsupervised learning |
title | Point Cloud Upsampling Algorithm: A Systematic Review |
title_full | Point Cloud Upsampling Algorithm: A Systematic Review |
title_fullStr | Point Cloud Upsampling Algorithm: A Systematic Review |
title_full_unstemmed | Point Cloud Upsampling Algorithm: A Systematic Review |
title_short | Point Cloud Upsampling Algorithm: A Systematic Review |
title_sort | point cloud upsampling algorithm a systematic review |
topic | point cloud upsampling deep learning generative adversarial network (GAN) graph convolutional network (GCN) unsupervised learning |
url | https://www.mdpi.com/1999-4893/15/4/124 |
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