Research Article
MANet: End-to-End Learning for Point Cloud Based on Robust Pointpillar and Multiattention
Table 3
Results on the KITTI test 3D detection benchmark.
| Methods | Mod | Bev mAP | Car | Pedestrian | Cyclist | Easy | Mod | Hard | Easy | Mod | Hard | Easy | Mod | Hard |
| MV3D [38] | Lidar&img | N/A | 71.09 | 62.35 | 55.12 | | | | | | | F-P [9] | 57.35 | 81.20 | 70.39 | 62.19 | 51.21 | 44.89 | 40.23 | 71.96 | 56.77 | 50.39 | VoxelNet [3] | Lidar | 49.05 | 77.47 | 65.11 | 57.73 | 39.48 | 33.69 | 31.5 | 61.22 | 48.36 | 44.37 | SECOND [39] | 56.69 | 83.13 | 73.66 | 66.20 | 51.07 | 42.56 | 37.29 | 70.51 | 53.85 | 46.90 | Pp [6] | 59.20 | 79.05 | 74.99 | 68.30 | 52.08 | 43.53 | 41.49 | 75.78 | 59.07 | 52.92 | TANet [7] | 62 | 83.81 | 75.38 | 67.66 | 54.92 | 46.67 | 38.63 | 73.93 | 59.60 | 53.59 | MANet | 65.11 | 83.47 | 78.85 | 71.89 | 56.02 | 51.74 | 45.58 | 80.08 | 64.74 | 60.79 |
|
|