3D Object Detection On Kitti Pedestrian Hard
Métriques
AP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AP | Paper Title | Repository |
|---|---|---|---|
| F-PointNet++ [Qi:2018fd] | 53.59 | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| PVCNN | 56.78 | Point-Voxel CNN for Efficient 3D Deep Learning | |
| M3DeTR | 56.49 | M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers | |
| F-PointNet [Qi:2018fd] | 49.28 | Frustum PointNets for 3D Object Detection from RGB-D Data |
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