Papers › PETR: Position Embedding Transformation for Multi-View 3D Object Detection
PETR: Position Embedding Transformation for Multi-View 3D Object Detection
Yingfei Liu, Tiancai Wang, Xiangyu Zhang, Jian Sun
In this paper, we develop position embedding transformation (PETR) for multi-view 3D object detection. PETR encodes the position information of 3D coordinates into image features, producing the 3D position-aware features. Object query can perceive the 3D position-aware features and perform end-to-end object detection. PETR achieves state-of-the-art performance (50.4% NDS and 44.1% mAP) on standard nuScenes dataset and ranks 1st place on the benchmark. It can serve as a simple yet strong baseline for future research. Code is available at \url{https://github.com/megvii-research/PETR}.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | 3D Object Detection on Argoverse2 Camera Only | PETR | Average mAP | 17.6 | #3 of 3 | Archive leaderboard | report |
| 3D Object Detection | TruckScenes | PETR | NDS | 12.1 | #3 of 4 | Archive leaderboard | report |
| 3D Object Detection | TruckScenes | PETR | mAP | 2.2 | #3 of 4 | Archive leaderboard | report |
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