3D Object Detection On Kitti Cars Hard Val
Métriques
AP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | AP |
|---|---|
| point-voxel-cnn-for-efficient-3d-deep | 63.81 |
| frustum-pointnets-for-3d-object-detection | 62.56 |
| multi-view-3d-object-detection-network-for | 56.56 |
| pv-rcnn-point-voxel-feature-set-abstraction-1 | 82.69 |
| svga-net-sparse-voxel-graph-attention-network | 79.15 |
| voxel-r-cnn-towards-high-performance-voxel | 78.93 |
| accurate-3d-object-detection-using-energy | 82.23 |
| probabilistic-and-geometric-depth-detecting | 16.9 |
| m3detr-multi-representation-multi-scale | 82.85 |
| pc-rgnn-point-cloud-completion-and-graph | 80.45 |