Research Article
MANet: End-to-End Learning for Point Cloud Based on Robust Pointpillar and Multiattention
Table 2
Results on the KITTI test BEV detection benchmark.
| Methods | 3D mAP | Car | Pedestrian | Cyclist | Easy | Mod | Hard | Easy | Mod | Hard | Easy | Mod | Hard |
| VoxelNet [3] | 58.25 | 89.25 | 79.26 | 77.39 | 46.13 | 40.74 | 38.11 | 66.70 | 54.76 | 50.55 | Pp [6] | 66.19 | 88.35 | 86.10 | 79.83 | 58.6 | 50.23 | 47.19 | 79.14 | 62.25 | 56.00 | F-P [9] | 65.39 | 88.70 | 84.00 | 75.33 | 58 | 50.22 | 47.20 | 75.38 | 61.96 | 54.98 | PIXOR [12] | N/A | 89.38 | 87.30 | 77.97 | | | | | | | MV3D [38] | N/A | 86.02 | 76.90 | 68.49 | | | | | | | SECOND [39] | 60.56 | 88.07 | 79.37 | 77.95 | 55.1 | 46.27 | 44.76 | 73.67 | 56.04 | 48.78 | MANet | 69.91 | 89.21 | 86.36 | 83.10 | 61.4 | 55.01 | 51.23 | 82.81 | 68.36 | 63.76 |
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