Partial Point Cloud Matching On 4Dmatch
Metriken
IR
NFMR
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | IR | NFMR | Paper Title | Repository |
|---|---|---|---|---|
| D3Feat (1000) | 52.7 | 51.6 | Lepard: Learning partial point cloud matching in rigid and deformable scenes | - |
| Li and Harada (θc=0.05) | 80.9 | 83.9 | Lepard: Learning partial point cloud matching in rigid and deformable scenes | - |
| Predator (3000) | 60.4 | 56.4 | Lepard: Learning partial point cloud matching in rigid and deformable scenes | - |
| D3Feat (3000) | 54.7 | 55.5 | Lepard: Learning partial point cloud matching in rigid and deformable scenes | - |
| Predator (1000) | 60 | 53.3 | Lepard: Learning partial point cloud matching in rigid and deformable scenes | - |
| Predator (5000) | 59.3 | 56.8 | Lepard: Learning partial point cloud matching in rigid and deformable scenes | - |
| D3Feat (5000) | 55.3 | 56.1 | Lepard: Learning partial point cloud matching in rigid and deformable scenes | - |
| Li and Harada (θc=0.1) | 82.7 | 83.7 | Lepard: Learning partial point cloud matching in rigid and deformable scenes | - |
| Li and Harada (θc=0.2) | 85.4 | 82.2 | Lepard: Learning partial point cloud matching in rigid and deformable scenes | - |
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