3D Object Detection On Kitti Cyclist Moderate
Metrics
AP
Results
Performance results of various models on this benchmark
Model Name | AP | Paper Title | Repository |
|---|---|---|---|
| F-PointNet++ [Qi:2018fd] | 56.49 | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| F-PointNet [Qi:2018fd] | 55.95 | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| M3DeTR | 71.70 | M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers | |
| PVCNN | 59.97 | Point-Voxel CNN for Efficient 3D Deep Learning |
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