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...

Full description

Saved in:
Bibliographic Details
Published in:Algorithms Vol. 15; no. 4; p. 124
Main Authors: Zhang, Yan, Zhao, Wenhan, Sun, Bo, Zhang, Ying, Wen, Wen
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.04.2022
Subjects:
Algorithms
Datasets
Deep learning
generative adversarial network (GAN)
graph convolutional network (GCN)
Image processing
Machine learning
Optimization
Performance evaluation
point cloud upsampling
unsupervised learning
ISSN:1999-4893, 1999-4893
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1999-4893
1999-4893
DOI:10.3390/a15040124