3D Object Detection On Kitti Pedestrians Hard
Métriques
AP
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | AP | Paper Title | Repository |
|---|---|---|---|
| STD | 41.97% | STD: Sparse-to-Dense 3D Object Detector for Point Cloud | - |
| IPOD | 42.39% | IPOD: Intensive Point-based Object Detector for Point Cloud | - |
| VoxelNet | 31.51% | VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection | |
| F-ConvNet | 41.49% | Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection | |
| Frustrum-PointPillars | 39.28 % | Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR | |
| M3DeTR | 38.75% | M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers | |
| Frustum PointNets | 40.23% | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| SVGA-Net | 44.56% | SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds | - |
| AVOD + Feature Pyramid | 40.88% | Joint 3D Proposal Generation and Object Detection from View Aggregation |
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