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点云场景下基于结构感知的车辆检测
引用本文:李宗民,姚纯纯,刘玉杰,李华.点云场景下基于结构感知的车辆检测[J].计算机辅助设计与图形学学报,2021,33(3):405-412.
作者姓名:李宗民  姚纯纯  刘玉杰  李华
作者单位:中国石油大学(华东)计算机科学与技术学院 青岛 266580;中国石油大学胜利学院 东营 257061;中国石油大学(华东)计算机科学与技术学院 青岛 266580;中国石油大学(华东)计算机科学与技术学院 青岛 266580;中国科学院计算技术研究所智能信息处理重点实验室 北京 100190;中国科学院大学 北京 100049
基金项目:山东省自然科学基金;中央高校基本科研业务费专项资金;国家自然科学基金
摘    要:在自动驾驶领域,计算机对周围环境的感知和理解是必不可少的.其中,相比于二维目标检测,三维点云目标检测可以提供二维目标检测所不具有的物体的三维方位信息,这对于安全自动驾驶是至关重要的.针对三维目标检测中原始输入点云到检测结果之间跨度大的问题,首先,提出了基于结构感知的候选区域生成模块,其中定义了每个点的结构特征,充分利用...

关 键 词:三维点云目标检测  结构特征  候选区域生成网络

Vehicle Detection Based on Structure Perception in Point Cloud
Li Zongmin,Yao Chunchun,Liu Yujie,Li Hua.Vehicle Detection Based on Structure Perception in Point Cloud[J].Journal of Computer-Aided Design & Computer Graphics,2021,33(3):405-412.
Authors:Li Zongmin  Yao Chunchun  Liu Yujie  Li Hua
Affiliation:(College of Computer Science and Technology,China University of Petroleum,Qingdao 266580;Shengli College of China University of Petroleum,Dongying 257061;Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049)
Abstract:In the field of automatic driving,computer perception and understanding of the surrounding environment is essential.Compared with 2D object detection,3D point cloud object detection can provide the three-dimensional information of the object that the 2D object detection does not have.In order to solve the problem of large disparity between the original input point cloud and the detection result in 3D object detection,a region proposal generation module based on structure awareness is proposed,in which the structural features of each point are defined,and the supervision information provided by the 3D point cloud object detection dataset is fully utilized.The network can learn more discriminative features to improve the quality of proposals.Secondly,the feature is added to the proposal fine-tuning stage to enrich the context features and local features of point cloud.Evaluated on KITTI 3D object detection dataset,in the region proposal generation stage,under the IoU threshold of 0.7,using 50 proposals,there is a more than 13%increase in the recall rate compared to previous results.In the proposal fine-tuning stage,the detection results of the 3 difficulty levels objects is obviously improved,indicating the effectiveness of the proposed method for 3D point cloud object detection.
Keywords:3D point cloud object detection  structure feature  region proposal network
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