Name
Title | ||
|---|---|---|
Learning Efficient Rotation Representation for Point Cloud via Local-Global Aggregation |
Abstract | ||
|---|---|---|
Recently, there have been attempted to solve the problem of rotation perturbation in point cloud analysis. However, most of them fail to exploit the long-distance context and lose global location information. To address this issue, we propose a novel rotation-invariant network called LGANet, which is assembled with two key modules: local representation learning module and global alignment module. ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICME51207.2021.9428170 | 2021 IEEE International Conference on Multimedia and Expo (ICME) |
Keywords | DocType | ISBN |
Deep learning,Three-dimensional displays,Perturbation methods,Conferences,Robustness,Rotation measurement | Conference | 978-1-6654-3864-3 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruibin Gu | 1 | 0 | 0.34 |
| Qiuxia Wu | 2 | 9 | 3.20 |
| Hongbin Xu | 3 | 0 | 1.69 |
| Wing W.Y. Ng | 4 | 0 | 0.34 |
| Zhiyong Wang | 5 | 550 | 51.76 |