Birds Eye View Object Detection On Kitti 1
Metrics
AP
Results
Performance results of various models on this benchmark
Model Name | AP | Paper Title | Repository |
|---|---|---|---|
| PointPillars | 50.23% | PointPillars: Fast Encoders for Object Detection from Point Clouds | - |
| Frustrum-PointPillars | 52.23 % | Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR | |
| VoxelNet | 40.74% | VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection | - |
| F-PointNet | 50.22% | Frustum PointNets for 3D Object Detection from RGB-D Data | - |
| STD | 51.39% | STD: Sparse-to-Dense 3D Object Detector for Point Cloud | - |
| AVOD-FPN | 51.05% | Joint 3D Proposal Generation and Object Detection from View Aggregation | - |
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