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

被引：272
作者：
Liu, Yingfei [1 ]
Wang, Tiancai [1 ]
Zhang, Xiangyu [1 ]
Sun, Jian [1 ]
机构：
[1] MEGVII Technol, Beijing, Peoples R China
来源：
COMPUTER VISION - ECCV 2022, PT XXVII | 2022年 / 13687卷
基金：
国家重点研发计划;
关键词：
Position embedding; Transformer; 3D object detection;
D O I：
10.1007/978-3-031-19812-0_31
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：531 / 548
页数：18
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