3D Object Detection On Kitti Pedestrians Hard
Metriken
AP
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | AP |
|---|---|
| std-sparse-to-dense-3d-object-detector-for | 41.97% |
| ipod-intensive-point-based-object-detector | 42.39% |
| voxelnet-end-to-end-learning-for-point-cloud | 31.51% |
| frustum-convnet-sliding-frustums-to-aggregate | 41.49% |
| frustum-pointpillars-a-multi-stage-approach | 39.28 % |
| m3detr-multi-representation-multi-scale | 38.75% |
| frustum-pointnets-for-3d-object-detection | 40.23% |
| svga-net-sparse-voxel-graph-attention-network | 44.56% |
| joint-3d-proposal-generation-and-object | 40.88% |