Knowledge Commons of Institute of Automation,CAS
V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection with BEV Global Non-Maximum Suppression | |
Zhang Caiji1,2![]() ![]() ![]() ![]() | |
2024-04 | |
会议名称 | the 35th IEEE Intelligent Vehicles Symposium |
会议日期 | June 2-5, 2024 |
会议地点 | Jeju Island, South Korea |
出版者 | IEEE |
摘要 | In recent years, research on Vehicle-to-Everything (V2X) collaborative perception algorithms mainly focuses on the fusion of intermediate features from LiDAR point clouds. Since the emergence of excellent single-vehicle visual perception models like BEVFormer, collaborative perception schemes based on camera and late-fusion have become feasible approaches. This paper proposes a V2X-collaborative 3D object detection structure in Bird’s Eye View (BEV) space, based on global non-maximum suppression and late-fusion (V2X-BGN), and conducts experiments on the V2X-Set dataset. Focusing on complex road conditions with extreme occlusion, the paper compares the camera-based algorithm with the LiDAR-based algorithm, validating the effectiveness of pure visual solutions in the collaborative 3D object detection task. Additionally, this paper highlights the complementary potential of camera-based and LiDAR-based approaches and the importance of objectto-ego vehicle distance in the collaborative 3D object detection task. |
关键词 | V2X |
收录类别 | EI |
语种 | 英语 |
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 | 环境多维感知 |
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/173211/57597 |
专题 | 多模态人工智能系统全国重点实验室 |
通讯作者 | Tian Bin |
作者单位 | 1.University of Chinese Academy of Sciences(UCAS) 2.Institute of Automation, Chinese Academy of Sciences 3.Hebei University of Engineering, School of Mechanical and Equipment Engineering 4.Waytous 5.Chongqing Iron and Steel Group Mining Co., Ltd. |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhang Caiji,Tian Bin,Meng Shi,et al. V2X-BGN: Camera-based V2X-Collaborative 3D Object Detection with BEV Global Non-Maximum Suppression[C]:IEEE,2024. |
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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 | ||
iv24.pdf(1659KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 |
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