Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/a15040124 | ALGORITHMS |
Keywords | DocType | Volume |
point cloud upsampling, deep learning, generative adversarial network (GAN), graph convolutional network (GCN), unsupervised learning | Journal | 15 |
Issue | ISSN | Citations |
4 | 1999-4893 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Zhang | 1 | 133 | 30.68 |
| Wenhan Zhao | 2 | 0 | 0.34 |
| Bo Sun | 3 | 0 | 0.34 |
| Ying Zhang | 4 | 163 | 25.25 |
| Wen Wen | 5 | 0 | 0.34 |