3D Object Detection On Kitti Cars Moderate 1
Métriques
AP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | AP |
|---|---|
| multi-view-3d-object-detection-network-for | 62.68 |
| m3detr-multi-representation-multi-scale | 85.41 |
| pc-rgnn-point-cloud-completion-and-graph | 81.43 |
| frustum-pointnets-for-3d-object-detection | 69.28 |
| svga-net-sparse-voxel-graph-attention-network | 80.23 |
| probabilistic-and-geometric-depth-detecting | 18.34 |
| voxel-r-cnn-towards-high-performance-voxel | 84.52 |
| deformable-pv-rcnn-improving-3d-object | 83.3 |
| pv-rcnn-point-voxel-feature-set-abstraction-1 | 84.83 |
| point-voxel-cnn-for-efficient-3d-deep | 71.54 |
| accurate-3d-object-detection-using-energy | 86.83 |