3D Object Detection On Kitti Pedestrian Easy
Metrics
AP
Results
Performance results of various models on this benchmark
Model Name | AP | Paper Title | Repository |
|---|---|---|---|
| PVCNN | 73.2 | Point-Voxel CNN for Efficient 3D Deep Learning | - |
| F-PointNet++ [Qi:2018fd] | 70.00 | Frustum PointNets for 3D Object Detection from RGB-D Data | - |
| F-PointNet [Qi:2018fd] | 65.08 | Frustum PointNets for 3D Object Detection from RGB-D Data | - |
| M3DeTR | 67.64 | M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers | - |
0 of 4 row(s) selected.