Institutional Repository of Chinese Acad Sci, Inst Automat, Res Ctr Precis Sensing & Control, Beijing 100190, Peoples R China
| Category-Level 6D Object Pose Estimation With Structure Encoder and Reasoning Attention | |
Liu, Jierui1,2; Cao, Zhiqiang1,2 ; Tang, Yingbo1,2; Liu, Xilong1,2; Tan, Min1,2
| |
| Source Publication | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
 |
| ISSN | 1051-8215 |
| 2022-10-01 | |
| Volume | 32Issue:10Pages:6728-6740 |
| Corresponding Author | Liu, Xilong(xilong.liu@ia.ac.cn) |
| Abstract | 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. |
| Keyword | Shape Three-dimensional displays Cognition Pose estimation Feature extraction Decoding Solid modeling Category-level 6D object pose estimation structure encoder reasoning attention |
| DOI | 10.1109/TCSVT.2022.3169144 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[62073322] ; National Natural Science Foundation of China[61836015] |
| Funding Organization | National Natural Science Foundation of China |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Electrical & Electronic |
| WOS ID | WOS:000864197600021 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/50359 |
| Collection | 精密感知与控制研究中心_精密感知与控制 复杂系统管理与控制国家重点实验室_先进机器人 复杂系统管理与控制国家重点实验室_互联网大数据与信息安全 |
| Corresponding Author | Liu, Xilong |
| Affiliation | 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 |
| First Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation 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. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment