3D Object Detection On Kitti Cyclist Moderate
Métriques
AP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AP | Paper Title | Repository |
|---|---|---|---|
| M3DeTR | 71.70 | M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers | |
| F-PointNet++ [Qi:2018fd] | 56.49 | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| F-PointNet [Qi:2018fd] | 55.95 | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| PVCNN | 59.97 | Point-Voxel CNN for Efficient 3D Deep Learning |
0 of 4 row(s) selected.