Research Article
Pillar-Based 3D Object Detection from Point Cloud with Multiattention Mechanism
Table 5
Results on the KITTI test 3D detection benchmark.
| Model | Speed (Hz) | mAP | Car | Pedestrian | Cyclist | Mod. | Easy (%) | Mod. (%) | Hard (%) | Easy (%) | Mod. (%) | Hard (%) | Easy (%) | Mod. (%) | Hard (%) |
| MV3D [36] | 2.8 | N/A | 71.09 | 62.35 | 55.12 | N/A | N/A | N/A | N/A | N/A | N/A | Cont-Fuse [37] | 16.7 | N/A | 82.54 | 66.22 | 64.04 | N/A | N/A | N/A | N/A | N/A | N/A | Roarnet [38] | 10 | N/A | 83.71 | 73.04 | 59.16 | N/A | N/A | N/A | N/A | N/A | N/A | AVOD-FPN [39] | 10 | 55.62 | 81.94 | 71.88 | 66.38 | 50.80 | 42.81 | 40.88 | 64.00 | 52.18 | 46.61 | F-PointNet [40] | 5.9 | 57.35 | 81.20 | 70.39 | 62.19 | 51.21 | 44.89 | 40.23 | 71.96 | 56.77 | 50.39 | VoxelNet | 4.4 | 49.05 | 77.47 | 65.11 | 57.73 | 39.48 | 33.69 | 31.50 | 61.22 | 48.36 | 44.37 | SECOND | 20 | 56.69 | 83.13 | 73.66 | 66.20 | 51.07 | 42.56 | 37.29 | 70.51 | 53.85 | 46.90 | PointPillars | 62 | 59.20 | 79.05 | 74.99 | 68.30 | 52.08 | 43.53 | 41.49 | 75.78 | 59.07 | 52.92 | Ours | 34 | 63.08 | 87.48 | 76.22 | 73.55 | 54.78 | 49.45 | 43.07 | 81.69 | 63.58 | 60.55 |
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