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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)
Department机器人学研究室
Conference Name32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017
Conference DateMay 19-21, 2017
Conference PlaceHefei, China
Source PublicationProceedings - 2017 32nd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages663-668
Indexed ByEI ; CPCI(ISTP)
EI Accession number20173204024259
WOS IDWOS:000425862800126
Contribution Rank1
ISBN9781538629017
KeywordDeep Learning Volumetric Representation Hough Forest And 3d Object Recognition
AbstractRobust 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.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/20820
Collection机器人学研究室
Corresponding AuthorLiu HS(刘洪森)
AffiliationState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shenyang, 110016, China
Recommended Citation
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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