论文标题
PETR：多视图3D对象检测的位置嵌入转换
PETR: Position Embedding Transformation for Multi-View 3D Object Detection
论文作者
论文摘要
在本文中，我们开发了用于多视图3D对象检测的位置嵌入转换（PETR）。 PETR将3D坐标的位置信息编码为图像特征，从而产生3D位置感知功能。对象查询可以感知3D位置感知功能并执行端到端对象检测。 PETR在标准Nuscenes数据集上实现了最先进的性能（50.4％NDS和44.1％的地图），并在基准中排名第一。它可以作为未来研究的简单但强大的基线。代码可在\ url {https://github.com/megvii-research/petr}中获得。
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}.
