Point Cloud Registration On Kitti
Metrics
Success Rate
Results
Performance results of various models on this benchmark
Model Name | Success Rate | Paper Title | Repository |
|---|---|---|---|
| 3DFeat-Net | 95.97 | 3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration | - |
| FCGF | 96.57 | Fully Convolutional Geometric Features | |
| DIP | 97.30 | Distinctive 3D local deep descriptors | - |
| GeDi | 99.82 | Learning general and distinctive 3D local deep descriptors for point cloud registration | - |
| D3Feat-pred | 99.81 | D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features | - |
| SpinNet | 99.10 | SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration | - |
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