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| Distinguishing and Matching-Aware Unsupervised Point Cloud Completion | |
Xiao, Haihong1; Li YQ(李玉琼)2 ; Kang, Wenxiong1,3,4; Wu, Qiuxia5
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| Corresponding Author | Li, Yuqiong([email protected]) ; Kang, Wenxiong([email protected]) |
| Source Publication | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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| 2023-09-01 | |
| Volume | 33Issue:9Pages:5160-5173 |
| ISSN | 1051-8215 |
| Abstract | Real-scanned point clouds are often incomplete due to occlusion, light reflection and limitations of sensor resolution, which impedes the related progress of downstream tasks, e.g., shape classification and object detection. Although there has been impressive research progress on the point cloud completion topic, they rely on the premise of extensive paired training data. However, collecting complete point clouds in some specified scenarios is labor-intensive and even impractical. To mitigate this problem, we propose DMNet, a distinguishing and matching-aware unsupervised point cloud completion network. Our work belongs to the group of unsupervised completion methods but goes beyond previous studies. Firstly, we propose a distinguishing-aware feature extractor to learn discriminable semantic information for different instances, simultaneously enhancing the robust invariant representation under noise disturbances. Secondly, we design a hierarchy-aware hyperbolic decoder to recover the complete geometry of point clouds, which not only can capture the implicit hierarchical relationships in data but also has an explicit extended nature. Finally, we develop a matching-aware refiner to eliminate noise points via aligning the topology structure of the input and predicted partial point clouds. Extensive experiments on MVP, Completion3D and KITTI datasets prove the effectiveness of our method, which performs favorably over state-of-the-art methods both quantitatively and qualitatively. |
| Keyword | Deep learning point cloud completion 3D vision |
| DOI | 10.1109/TCSVT.2023.3250970 |
| Indexed By | SCI ; EI |
| Language | 英语 |
| WOS ID | WOS:001063316800053 |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Electrical & Electronic |
| Funding Project | Youth Innovation Promotion Association of the Chinese Academy of Sciences[2018024] ; National Natural Science Foundation of China[61976095] ; National Natural Science Foundation of China[61575209] ; Experiments for Space Exploration Program, Qian Xuesen Laboratory, China Academy of Space Technology[TKTSPY-2020-05-01] |
| Funding Organization | Youth Innovation Promotion Association of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; Experiments for Space Exploration Program, Qian Xuesen Laboratory, China Academy of Space Technology |
| Classification | 一类 |
| Ranking | 1 |
| Contributor | Li, Yuqiong ; Kang, Wenxiong |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://dspace.imech.ac.cn/handle/311007/92988 |
| Collection | 中国科学院力学研究所 |
| Affiliation | 1.South China Univ Technol, Sch Automat Sci & Engn, Guangzhou 511442, Peoples R China; 2.Chinese Acad Sci, Key Lab Mech Fluid Solid Coupling Syst, Inst Mech, Beijing 100190, Peoples R China; 3.South China Univ Technol, Sch Future Technol, Guangzhou 510641, Peoples R China; 4.Young Scholar Project Ctr, Pazhou Lab, Guangzhou 510335, Peoples R China; 5.South China Univ Technol, Sch Software Engn, Guangzhou 510006, Peoples R China |
| Recommended Citation GB/T 7714 | Xiao, Haihong,Li YQ,Kang, Wenxiong,et al. Distinguishing and Matching-Aware Unsupervised Point Cloud Completion[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2023,33,9,:5160-5173.Rp_Au:Li, Yuqiong, Kang, Wenxiong |
| APA | Xiao, Haihong,李玉琼,Kang, Wenxiong,&Wu, Qiuxia.(2023).Distinguishing and Matching-Aware Unsupervised Point Cloud Completion.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,33(9),5160-5173. |
| MLA | Xiao, Haihong,et al."Distinguishing and Matching-Aware Unsupervised Point Cloud Completion".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 33.9(2023):5160-5173. |
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| File Name/Size | DocType | Version | Access | License | ||
| Jp2023Fa341.pdf（3422KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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