Knowledge Commons of Institute of Automation,CAS
PolarFormer: Multi-Camera 3D Object Detection with Polar Transformer | |
Jiang, Yanqin1,4; Zhang, Li2; Miao, Zhenwei5; Zhu, Xiatian6; Gao, Jin1,4![]() ![]() | |
2023 | |
会议名称 | 37th AAAI Conference on Artificial Intelligence, AAAI 2023 |
会议日期 | February 7, 2023 - February 14, 2023 |
会议地点 | Washington, DC, United states |
摘要 | 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. |
收录类别 | EI |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 实体人工智能系统感认知 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57503 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
25185-Article Text-2(14499KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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