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

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arxiv 2203.05625 v3 pith:2FEFMHF4 submitted 2022-03-10 cs.CV

Yingfei Liu , Tiancai Wang , Xiangyu Zhang , Jian Sun This is my paper · ORCID
classification cs.CV
keywords petrobjectdetectionfeaturespositionembeddingmulti-viewposition-aware
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FDR-Occ: Factorized Dense Routing for Full-Spectrum 3D Occupancy Prediction

    cs.CV 2026-07 conditional novelty 7.0 of 10

    Factorized Dense Routing approximates unconstrained 2D-to-3D feature mixing by hierarchical tensor contractions, yielding global-context occupancy prediction that remains robust without camera extrinsics.

  2. 3D-MOOD: Lifting 2D to 3D for Monocular Open-Set Object Detection

    cs.CV 2025-07 conditional novelty 7.0 of 10

    3D-MOOD is the first end-to-end monocular 3D object detector for open-set classes and novel scenes, achieving SOTA on Omni3D and on new open-set benchmarks.

  3. InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

    cs.RO 2026-02 unverdicted novelty 6.0 of 10

    InCoM reports 23–28 percentage-point success-rate gains in mobile manipulation benchmarks by dynamically reweighting multi-scale perception via inferred motion intent and decoupling base-arm action generation with flo...

  4. ODG: Occupancy Prediction Using Dual Gaussians

    cs.CV 2025-06 conditional novelty 6.0 of 10

    ODG uses separate static and dynamic Gaussian query sets, refined coarse-to-fine, plus rendering supervision, and reports state-of-the-art occupancy prediction on Occ3D-nuScenes and Occ3D-Waymo.

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