Recent Advances on Application of Deep Learning for Recovering Object Pose
文献类型：会议论文
| 作者 | Li, Wanyi1 ; Luo, Yongkang1; Wang, Peng1; Qin, Zhengke1; Zhou, Hai2; Qiao, Hong3
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| 出版日期 | 2016-12 |
| 会议日期 | Dec. 3 – Dec. 7, 2016 |
| 会议地点 | Qingdao, China |
| 关键词 | Pose Estimation Deep Learning Survey |
| 英文摘要 | Recovering 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. |
| 会议录 | IEEE International Conference on Robotics and Biomimetics
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| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12113]  |
| 专题 | 智能机器人系统研究 |
| 通讯作者 | Li, Wanyi |
| 作者单位 | 1.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 |
| 推荐引用方式 GB/T 7714 | Li, Wanyi,Luo, Yongkang,Wang, Peng,et al. Recent Advances on Application of Deep Learning for Recovering Object Pose[C]. 见:. Qingdao, China. Dec. 3 – Dec. 7, 2016. |
入库方式： OAI收割
来源：自动化研究所
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