3D Object Detection On Kitti Pedestrians Easy
المقاييس
AP
النتائج
نتائج أداء النماذج المختلفة على هذا المعيار القياسي
جدول المقارنة
| اسم النموذج | AP |
|---|---|
| ipod-intensive-point-based-object-detector | 56.92% |
| voxelnet-end-to-end-learning-for-point-cloud | 39.48% |
| svga-net-sparse-voxel-graph-attention-network | 55.21% |
| std-sparse-to-dense-3d-object-detector-for | 53.08% |
| frustum-pointpillars-a-multi-stage-approach | 51.22 % |
| frustum-pointnets-for-3d-object-detection | 51.21% |
| frustum-convnet-sliding-frustums-to-aggregate | 52.37% |
| joint-3d-proposal-generation-and-object | 50.8% |
| m3detr-multi-representation-multi-scale | 47.05% |