Abstract
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 https://github.com/megvii-research/PETR.
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CITATION STYLE
Liu, Y., Wang, T., Zhang, X., & Sun, J. (2022). PETR: Position Embedding Transformation for Multi-view 3D Object Detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13687 LNCS, pp. 531–548). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19812-0_31
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