BEV Object Detection on KITTI (val)
95.44AP_BEV EasyContFusion
Evaluation results
| Method | Links | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| ContFusion2019.03 | 95.44 | — | — | — | — | — | — | — | — | — | 87.34 | 82.43 | |
| F-ConvNet2019.03 | 90.23 | — | — | — | — | — | — | — | — | — | 88.79 | 86.84 | |
| VoxelNet2019.03 | 89.6 | — | — | — | — | — | — | — | — | — | 84.81 | 78.57 | |
| IPOD2019.03 | 88.3 | — | — | — | — | — | — | — | — | — | 86.4 | 84.6 | |
| F-PointNet2019.03 | 88.16 | — | — | — | — | — | — | — | — | — | 84.92 | 76.44 | |
| MV3D2019.03 | 86.55 | — | — | — | — | — | — | — | — | — | 78.1 | 76.67 | |
| STONEBackbone detection model=SECOND [61], Query budget=1%2024.10 | 82.14 | — | — | — | — | — | — | — | — | — | 70.82 | 65.68 | |
| KECORBackbone detection model=SECOND [61], Query budget=1%2024.10 | 80 | — | — | — | — | — | — | — | — | — | 68.2 | 63.2 | |
| CRBBackbone detection model=SECOND [61], Query budget=1%2024.10 | 78.84 | — | — | — | — | — | — | — | — | — | 65.82 | 61.25 | |
| LLALBackbone detection model=SECOND [61], Query budget=1%2024.10 | 76.52 | — | — | — | — | — | — | — | — | — | 63.25 | 59.07 | |
| BADGEBackbone detection model=SECOND [61], Query budget=1%2024.10 | 76.07 | — | — | — | — | — | — | — | — | — | 63.39 | 59.47 | |
| BAITBackbone detection model=SECOND [61], Query budget=1%2024.10 | 76.04 | — | — | — | — | — | — | — | — | — | 63.49 | 58.4 | |
| RandomBackbone detection model=SECOND [61], Query budget=1%2024.10 | 75.66 | — | — | — | — | — | — | — | — | — | 63.77 | 59.71 | |
| CORESETBackbone detection model=SECOND [61], Query budget=1%2024.10 | 73.08 | — | — | — | — | — | — | — | — | — | 61.03 | 56.95 | |
| 3DOPData=Stereo2019.06 | — | 71.41 | 57.78 | 51.91 | 55.04 | 41.25 | 34.55 | 12.63 | 9.49 | 7.59 | — | — | |
| MF3DData=Mono2019.06 | — | — | — | — | 55.02 | 36.73 | 31.27 | 22.03 | 13.63 | 11.6 | — | — | |
| Mono3DData=Mono2019.06 | — | 32.76 | 25.15 | 23.65 | 30.5 | 22.39 | 19.16 | 5.22 | 5.19 | 4.13 | — | — | |
| MonoGRNetData=Mono2019.06 | — | 73.1 | 60.66 | 46.86 | 54.21 | 39.69 | 33.06 | 24.97 | 19.44 | 16.3 | — | — | |
| TLNetData=Stereo2019.06 | — | 81.11 | 65.25 | 58.15 | 62.46 | 45.99 | 41.92 | 29.22 | 21.88 | 18.83 | — | — | |
| TLNet (monocular baseline)Data=Mono2019.06 | — | 74.18 | 57.04 | 50.17 | 52.72 | 37.22 | 32.16 | 21.91 | 15.72 | 14.32 | — | — | |
| VeloFCNData=LiDAR2019.06 | — | — | — | — | 79.68 | 63.82 | 62.8 | 40.14 | 32.08 | 30.47 | — | — |