3D Object Detection On Kitti Cyclist Hard Val
Métriques
AP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AP | Paper Title | Repository |
|---|---|---|---|
| PVCNN | 56.24 | Point-Voxel CNN for Efficient 3D Deep Learning | |
| F-PointNet++ [Qi:2018fd] | 53.37 | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| F-PointNet [Qi:2018fd] | 52.65 | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| M3DeTR | 68.29 | M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers |
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