AFDet: Anchor Free One Stage 3D Object Detection
About
High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving. Most previous works try to solve it using anchor-based detection methods which come with two drawbacks: post-processing is relatively complex and computationally expensive; tuning anchor parameters is tricky. We are the first to address these drawbacks with an anchor free and Non-Maximum Suppression free one stage detector called AFDet. The entire AFDet can be processed efficiently on a CNN accelerator or a GPU with the simplified post-processing. Without bells and whistles, our proposed AFDet performs competitively with other one stage anchor-based methods on KITTI validation set and Waymo Open Dataset validation set.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| 3D Object Detection | Waymo Open Dataset (val) | -- | 175 | |
| 3D Object Detection | Waymo Open Dataset LEVEL_1 (val) | 3D AP63.69 | 46 | |
| 3D Object Detection (Vehicle) | Waymo Open Dataset LEVEL_1 (val) | 3D AP Overall63.69 | 34 | |
| 3D Vehicle Detection | Waymo Open Dataset v1.2 (val) | L1 3D mAP63.69 | 29 | |
| Vehicle Detection | Waymo Open Dataset LEVEL_1 v1.2 (val) | 3D AP63.69 | 28 | |
| 3D Object Detection (Vehicle) | Waymo Open Dataset (val) | -- | 14 | |
| 3D Object Detection | Waymo Open Dataset Vehicles (val) | -- | 13 | |
| 3D Object Detection | Waymo Open Dataset 202 sequences (val) | L1 3D mAP Overall63.69 | 6 |