Birds Eye View Object Detection On Kitti
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AP
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | AP | Paper Title | Repository |
|---|---|---|---|
| F-PointNet | 61.96% | Frustum PointNets for 3D Object Detection from RGB-D Data | |
| AVOD-FPN | 57.48% | Joint 3D Proposal Generation and Object Detection from View Aggregation | |
| PV-RCNN | 68.89% | PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection | |
| VoxelNet | 54.76% | VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection | |
| PointPillars | 62.25% | PointPillars: Fast Encoders for Object Detection from Point Clouds | |
| STD | 65.32% | STD: Sparse-to-Dense 3D Object Detector for Point Cloud | - |
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