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PETR: Position Embedding Transformation for Multi-view 3D Object Detection

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Computer Vision – ECCV 2022 (ECCV 2022)

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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.

Y. Liu and T. Wang—Equal contribution.

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Acknowledgements

This research was supported by National Key R &D Program of China (No. 2017YFA0700800) and Beijing Academy of Artificial Intelligence (BAAI).

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Liu, Y., Wang, T., Zhang, X., Sun, J. (2022). PETR: Position Embedding Transformation for Multi-view 3D Object Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13687. Springer, Cham. https://doi.org/10.1007/978-3-031-19812-0_31

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