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Deep learning of volumetric representation for 3D object recognition
Liu HS(刘洪森); Cong Y(丛杨); Tang YD(唐延东)
作者部门机器人学研究室
会议名称32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017
会议日期May 19-21, 2017
会议地点Hefei, China
会议录名称Proceedings - 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017
出版者IEEE
出版地New York
2017
页码663-668
收录类别EI ; CPCI(ISTP)
EI收录号20173204024259
WOS记录号WOS:000425862800126
产权排序1
ISBN号9781538629017
关键词Deep Learning Volumetric Representation Hough Forest And 3d Object Recognition
摘要Robust 3D object detection and pose estimation is still a big challenging for robot vision. In this paper, we propose a new framework for 3D object detection and pose estimation. Rather than using RGB-D image as the original data, we propose to use volumetric representation with the help of unsupervised deep learning network to extract low dimensional feature from 3D point cloud directly. The volumetric representation can not only eliminate the dense scale sampling for offline model training, but also reduce the distortion by mapping the 3D shape to 2D plane and overcome the dependence on texture information. Depending on the Hough forest, we can achieve multi-object detection and pose estimation simultaneously. In compare with the state-of-the-arts using public datasets, we justify the effectiveness of our proposed method.
语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/20820
专题机器人学研究室
通讯作者Liu HS(刘洪森)
作者单位State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shenyang, 110016, China
推荐引用方式
GB/T 7714
Liu HS,Cong Y,Tang YD. Deep learning of volumetric representation for 3D object recognition[C]. New York:IEEE,2017:663-668.
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