Name
Abstract | ||
|---|---|---|
•We present LGR-Net which considers local geometric features and global topology-preserving features to achieve rotation invariance.•The complementary relationship between shape descriptions and spatial attributes is adaptively exploited by an attention-based fusion module.•LGR-Net significantly outperforms state-of-the-art methods on both synthetic and real-world datasets undergoing random 3D rotations. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.patcog.2022.108626 | Pattern Recognition |
Keywords | DocType | Volume |
Point cloud analysis,Rotation invariance,Deep learning,Classification,Segmentation | Journal | 127 |
ISSN | Citations | PageRank |
0031-3203 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Chen | 1 | 12 | 11.41 |
| Jiaqi Yang | 2 | 102 | 10.71 |
| Xin Xiong | 3 | 0 | 0.34 |
| Angfan Zhu | 4 | 0 | 1.35 |
| Zhiguo Cao | 5 | 314 | 44.17 |
| Xin Li | 6 | 96 | 8.05 |