Name
Abstract | ||
|---|---|---|
Point cloud registration is a key problem for robotics and computer vision communities. This represents estimating a rigid transform which aligns one point cloud to another. Iterative closest point (ICP) is a well-known classical method for this problem, yet it generally achieves high alignment only when the source and template point cloud are mostly pre-aligned. If each point cloud is far away or... |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/LRA.2020.2970946 | IEEE Robotics and Automation Letters |
Keywords | DocType | Volume |
Three-dimensional displays,Feature extraction,Transforms,Computer architecture,Estimation,Deep learning,Network architecture | Journal | 5 |
Issue | ISSN | Citations |
3 | 2377-3766 | 2 |
PageRank | References | Authors |
0.38 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Akiyoshi Kurobe | 1 | 2 | 0.38 |
| Yusuke Sekikawa | 2 | 9 | 3.87 |
| Kohta Ishikawa | 3 | 2 | 0.38 |
| Hideo Saito | 4 | 1147 | 169.63 |