Name
Abstract | ||
|---|---|---|
•A new unsupervised encoder layer is able to extract 3D point-wise features directly.•Improving feature discrimination performance via geometric information preservation.•Improving robustness by stacking locally aggregated features on point-wise features.•Using 3D point cloud features better resists occlusion and background clutter. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2019.03.025 | Pattern Recognition |
Keywords | Field | DocType |
Stacked 3D feature encoder,3D object recognition,6-DOF pose estimation,Geometric information preservation | Pattern recognition,Pose,RGB color model,Encoder,Artificial intelligence,Point cloud,Robot,Discriminative model,Feature learning,Mathematics,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
92 | 1 | 0031-3203 |
Citations | PageRank | References |
2 | 0.36 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongsen Liu | 1 | 2 | 1.38 |
| Yang Cong | 2 | 684 | 38.22 |
| Chenguang Yang | 3 | 2213 | 138.71 |
| Y. Tang | 4 | 243 | 33.69 |