Object Localization On Kitti Pedestrians Hard
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Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | AP | Paper Title | Repository |
|---|---|---|---|
| Frustum PointNets | 47.2% | Frustum PointNets for 3D Object Detection from RGB-D Data | - |
| VoxelNet | 38.11% | VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection | - |
| Frustrum-PointPillars | 48.30 % | Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR |
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