Table 1.
3D detection and bird’s eye view detection performance: Average precision (AP) (%) of the car on the KITTI validation set.
| Type | Method | Modality | 3D Detection (IoU = 0.7) | BEV Detection (IoU = 0.7) | FPS | ||||
|---|---|---|---|---|---|---|---|---|---|
| Easy | Moderate | Hard | Easy | Moderate | Hard | ||||
| MV3D [20] | RGB + LiDAR | 71.29 | 62.68 | 56.56 | 86.55 | 78.10 | 76.67 | 3 | |
| AVOD-FPN [21] | RGB + LiDAR | 84.41 | 74.44 | 68.65 | N/A | N/A | N/A | 10 | |
| 2-stage | F-PointNet [22] | RGB + LiDAR | 83.76 | 70.92 | 63.65 | 88.16 | 84.02 | 76.44 | 6 |
| IPOD [30] | RGB + LiDAR | 84.10 | 76.40 | 75.30 | 88.30 | 86.40 | 84.60 | N/A | |
| PointRCNN [25] | LiDAR | 88.88 | 78.63 | 77.38 | N/A | N/A | N/A | 10 | |
| VoxelNet [11] | LiDAR | 81.97 | 65.46 | 62.85 | 89.60 | 84.81 | 78.57 | 4 | |
| SECOND [12] | LiDAR | 87.43 | 76.48 | 69.10 | 89.96 | 87.07 | 79.66 | 25 | |
| Pointpillars [13] | LiDAR | 86.13 | 77.03 | 72.43 | 89.93 | 86.92 | 84.97 | 62 | |
| 1-stage | 3DBN [31] | LiDAR | 87.98 | 77.89 | 76.35 | N/A | N/A | N/A | 8 |
| TANet [14] | LiDAR | 88.21 | 77.85 | 75.62 | N/A | N/A | N/A | 29 | |
| Voxel-FPN [32] | LiDAR | 88.27 | 77.86 | 75.84 | 90.20 | 87.92 | 86.27 | 50 | |
| PSANet (Ours) | LiDAR | 89.02 | 78.70 | 77.57 | 90.20 | 87.88 | 86.20 | 11 | |