Command Palette
Search for a command to run...
3D Object Detection On Kitti Pedestrian
Metrics
AP
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| PVCNN | 64.71 | Point-Voxel CNN for Efficient 3D Deep Learning | |
| F-PointNet++ [Qi:2018fd] | 61.32 | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| M3DeTR | 60.63 | M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers | |
| F-PointNet [Qi:2018fd] | 55.85 | Frustum PointNets for 3D Object Detection from RGB-D Data |
0 of 4 row(s) selected.