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PolarFormer: Multi-Camera 3D Object Detection with Polar Transformer
Jiang, Yanqin1,4; Zhang, Li2; Miao, Zhenwei5; Zhu, Xiatian6; Gao, Jin1,4; Hu, Weiming1,4,7; Jiang, Yu-Gang3
Conference Name37th AAAI Conference on Artificial Intelligence, AAAI 2023
Conference DateFebruary 7, 2023 - February 14, 2023
Conference PlaceWashington, DC, United states

3D object detection in autonomous driving aims to reason "what" and "where" the objects of interest present in a 3Dworld. Following the conventional wisdom of previous 2D object detection, existing methods often adopt the canonical Cartesian coordinate system with perpendicular axis. However, we conjugate that this does not fit the nature of the ego car’s perspective, as each onboard camera perceives the world in shape of wedge intrinsic to the imaging geometry with radical (non-perpendicular) axis. Hence, in this paper we advocate the exploitation of the Polar coordinate system and propose a new Polar Transformer (PolarFormer) for more accurate 3D object detectionin the bird’s-eye-view (BEV) taking as input only multi-camera 2D images. Specifically, we design a cross-attention based Polar detection head without restriction to the shape of input structure to deal with irregular Polar grids. For tackling the unconstrained object scale variations along Polar’s distance dimension, we further introduce a multi-scale Polar representation learning strategy. As a result, our model can make best use of the Polar representation rasterized via attending to the corresponding image observation in a sequence-to-sequence fashion subject to the geometric constraints. Thorough experiments on the nuScenes dataset demonstrate that our PolarFormeroutperforms significantly state-of-the-art 3D object detection alternatives.

Indexed ByEI
Sub direction classification目标检测、跟踪与识别
planning direction of the national heavy laboratory实体人工智能系统感认知
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Document Type会议论文
Affiliation1.NLPR, Institute of Automation, Chinese Academy of Sciences, China
2.School of Data Science, Fudan University, China
3.School of Computer Science, Fudan University, China
4.School of Artificial Intelligence, University of Chinese Academy of Sciences, China
5.Alibaba DAMO Academy
6.Surrey Institute for People-Centred Artificial Intelligence, CVSSP, University of Surrey, United Kingdom
7.School of Information Science and Technology, ShanghaiTech University, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Jiang, Yanqin,Zhang, Li,Miao, Zhenwei,et al. PolarFormer: Multi-Camera 3D Object Detection with Polar Transformer[C],2023.
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