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Recent Advances on Application of Deep Learning for Recovering Object Pose
Li, Wanyi1; Luo, Yongkang1; Wang, Peng1; Qin, Zhengke1; Zhou, Hai2; Qiao, Hong3
2016-12
Conference NameIEEE International Conference on Robotics and Biomimetics
Source PublicationIEEE International Conference on Robotics and Biomimetics
Conference DateDec. 3 – Dec. 7, 2016
Conference PlaceQingdao, China
AbstractRecovering object pose is of great importance to many higher level tasks such as robotic manipulation, scene understanding and augmented reality to name a few. Following the recent major breakthroughs in many computer vision tasks made by the deep learning, intensive research to experiment with it also in the task of recovering object pose is conducting. This paper aims to review the state-of-the-art progress on deep learning based pose estimation methods. Firstly, we introduce some popular datasets together with their relevant attributes. Secondly, the deep learning based pose estimation methods are summarized and categorized, and detailed descriptions of representative methods are provided, and their pros and cons are examined. Thirdly, evaluation protocol and comparable performance of reviewed approaches are given. Finally, we highlight the advantages of deep learning based pose estimation methods and provide insights for future.
KeywordPose Estimation Deep Learning Survey
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12113
Collection精密感知与控制研究中心_精密感知与控制
Corresponding AuthorLi, Wanyi
Affiliation1.Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences
2.Research Center of Laser Fusion, China Academy of Engineering Physics
3.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Wanyi,Luo, Yongkang,Wang, Peng,et al. Recent Advances on Application of Deep Learning for Recovering Object Pose[C],2016.
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