  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}.
