Research Article
Research on 3D Point Cloud Object Detection Algorithm for Autonomous Driving
Table 3
Comparison of 3D positioning performance between our algorithm and the state-of-the-art algorithm.
| Method | Modality | Car | Pedestrian | Cyclist | Easy | Moderate | Hard | Easy | Moderate | Hard | Easy | Moderate | Hard |
| PointPillars [22] | Only LiDAR | 90.07 | 86.56 | 82.81 | 57.60 | 48.64 | 45.78 | 79.90 | 62.73 | 55.58 | PointRCNN [33] | 92.13 | 87.39 | 82.72 | 54.77 | 48.13 | 42.84 | 82.56 | 67.24 | 60.28 | Part-A2 [29] | 94.07 | 85.35 | 75.88 | 59.04 | 49.81 | 45.92 | 83.43 | 68.73 | 61.85 | PV-RCNN [39] | 90.25 | 81.43 | 76.82 | 52.17 | 43.29 | 40.29 | 78.60 | 63.71 | 57.65 | SE-SSD [40] | 91.49 | 82.54 | 77.15 | - | - | - | - | - | - | AVOD-FPN [18] | RGB + LiDAR | 90.99 | 84.82 | 79.62 | 58.49 | 50.32 | 46.98 | 69.39 | 57.12 | 51.09 | F-PointNet [16] | 91.17 | 84.67 | 74.77 | 70.00 | 61.32 | 53.59 | 77.26 | 61.37 | 53.78 | ContFuse [20] | 94.07 | 85.35 | 75.88 | — | — | — | — | — | — | MV3D [15] | 86.62 | 78.93 | 69.80 | — | — | — | — | — | — | Ours | | 95.01 | 88.32 | 80.57 | 79.67 | 66.89 | 56.36 | 90.12 | 72.41 | 63.21 |
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