3D Point Cloud Classification On Intra
Metriken
F1 score (5-fold)
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | F1 score (5-fold) |
|---|---|
| pointconv-deep-convolutional-networks-on-3d | 0.883 |
| adaptive-graph-convolution-for-point-cloud | 0.858 |
| so-net-self-organizing-network-for-point | 0.868 |
| pointcnn-convolution-on-x-transformed-points | 0.875 |
| spidercnn-deep-learning-on-point-sets-with | 0.872 |
| 3d-medical-point-transformer-introducing | 0.936 |
| pointnet-deep-hierarchical-feature-learning | 0.903 |
| geometry-sharing-network-for-3d-point-cloud | 0.872 |
| dynamic-graph-cnn-for-learning-on-point | 0.738 |
| pct-point-cloud-transformer | 0.914 |
| pointnet-deep-learning-on-point-sets-for-3d | 0.684 |
| paconv-position-adaptive-convolution-with | 0.906 |