Performance comparison with state-of-the-art models on the KITTI val set for pedestrian 3D detection in all three difficulties and also the comparison of the GPU memory usage (MiB) and the inference time (FPS). All the 3D detection results are evaluated with average precision and calculated by 40 recall positions.
| Models | 3D Pedestrian | Mem. | FPS | ||
|---|---|---|---|---|---|
| Easy | Mod. | Hard | |||
| PV-RCNN, [24] | 62.72 | 54.51 | 49.87 | 8243 | 17 |
| CT3D, [23] | 61.04 | 55.55 | 51.05 | 6485 | 19 |
| PDV, [12] | 66.90 | 60.80 | 55.85 | 7302 | 21 |
| Ours | 68.53 | 61.01 | 56.41 | 5669 | 27 |
| Models | 3D Pedestrian | Mem. | FPS | ||
|---|---|---|---|---|---|
| Easy | Mod. | Hard | |||
| PV-RCNN, [ | 62.72 | 54.51 | 49.87 | 8243 | 17 |
| CT3D, [ | 61.04 | 55.55 | 51.05 | 6485 | 19 |
| PDV, [ | 66.90 | 60.80 | 55.85 | 7302 | 21 |
| Ours | |||||
Note: Our model’s results are shown in bold, and the best results are underlined. Also, the above results are reproduced by the publicly release model ([28]).
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