Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention

doi:10.1109/TCSVT.2022.3169144

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进机器人控制团队

	Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention
	Liu, Jierui 1,2; Cao, Zhiqiang 1,2; Tang, Yingbo 1,2; Liu, Xilong 1,2; Tan, Min 1,2
刊名	IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
	2022-10-01
卷号	32 期号:10 页码:6728-6740
关键词	Shape Three-dimensional displays Cognition Pose estimation Feature extraction Decoding Solid modeling Category-level 6D object pose estimation structure encoder reasoning attention
ISSN号	1051-8215
DOI	10.1109/TCSVT.2022.3169144
通讯作者	Liu, Xilong(xilong.liu@ia.ac.cn)
英文摘要	Category-level 6D object pose estimation has gained popularity and it is still challenging due to the diversity of different instances within the same category. In this paper, a novel category-level 6D object pose estimation framework with structure encoder and reasoning attention is proposed. A structure autoencoder is introduced to mine the shared structure features in the color images within the same category, via a distinct learning strategy that recovers the image of another instance but with the most similar pose to the input. On this basis, a reasoning attention decoder and full connected layers are stacked to form a rotation prediction network, where the structure features and 3D shape features are integrated and projected to a semantic space. The semantic space includes observed patterns and learnable patterns, which are better learned by adding a shortcut connection branch parallel to reasoning attention decoder with gradient decouple. Further reasoning based on these patterns endows the decoder with powerful feature representation. Without 3D object models, the proposed method models the attributes of category implicitly in the semantic space and better performance of 6D object pose estimation is guaranteed by reasoning on this space. The effectiveness of the proposed method is verified by the results on public datasets and actual experiments.
资助项目	National Natural Science Foundation of China[62073322] ; National Natural Science Foundation of China[61836015]
WOS研究方向	Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000864197600021
资助机构	National Natural Science Foundation of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/50358]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Liu, Xilong
作者单位	1.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Liu, Jierui,Cao, Zhiqiang,Tang, Yingbo,et al. Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2022,32(10):6728-6740.
APA	Liu, Jierui,Cao, Zhiqiang,Tang, Yingbo,Liu, Xilong,&Tan, Min.(2022).Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,32(10),6728-6740.
MLA	Liu, Jierui,et al."Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 32.10(2022):6728-6740.