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Efficient 3D object recognition via geometric information preservation
Liu HS(刘洪森)1,2,3; Cong Y(丛杨)1,2; Yang, Chenguang4; Tang YD(唐延东)1,2
Department机器人学研究室
Source PublicationPattern Recognition
ISSN0031-3203
2019
Volume92Pages:135-145
Indexed BySCI ; EI
EI Accession number20191306715542
WOS IDWOS:000468013000011
Contribution Rank1
Funding OrganizationNature Science Foundation of China ; CAS-Youth Innovation Promotion Association Scholarship
KeywordStacked 3D feature encoder 3D object recognition 6-DOF pose estimation Geometric information preservation
AbstractAccurate 3D object recognition and 6-DOF pose estimation have been pervasively applied to a variety of applications, such as unmanned warehouse, cooperative robots, and manufacturing industry. How to extract a robust and representative feature from the point clouds is an inevitable and important issue. In this paper, an unsupervised feature learning network is introduced to extract 3D keypoint features from point clouds directly, rather than transforming point clouds to voxel grids or projected RGB images, which saves computational time while preserving the object geometric information as well. Specifically, the proposed network features in a stacked point feature encoder, which can stack the local discriminative features within its neighborhoods to the original point-wise feature counterparts. The main framework consists of both offline training phase and online testing phase. In the offline training phase, the stacked point feature encoder is trained first and then generate feature database of all keypoints, which are sampled from synthetic point clouds of multiple model views. In the online testing phase, each feature extracted from the unknown testing scene is matched among the database by using the K-D tree voting strategy. Afterwards, the matching results are achieved by using the hypothesis & verification strategy. The proposed method is extensively evaluated on four public datasets and the results show that ours deliver comparable or even superior performances than the state-of-the-arts in terms of F1-score, Average of the 3D distance (ADD) and Recognition rate.
Language英语
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS KeywordFEATURES ; FRAMEWORK
WOS Research AreaComputer Science ; Engineering
Funding ProjectNature Science Foundation of China[61722311] ; Nature Science Foundation of China[U1613214] ; Nature Science Foundation of China[61821005] ; Nature Science Foundation of China[61533015] ; CAS-Youth Innovation Promotion Association Scholarship[2012163]
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Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/24474
Collection机器人学研究室
Corresponding AuthorCong Y(丛杨)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, 100049, China
4.Bristol Robotics Laboratory, University of the West of England, Bristol, BS16 1QY, United Kingdom
Recommended Citation
GB/T 7714
Liu HS,Cong Y,Yang, Chenguang,et al. Efficient 3D object recognition via geometric information preservation[J]. Pattern Recognition,2019,92:135-145.
APA Liu HS,Cong Y,Yang, Chenguang,&Tang YD.(2019).Efficient 3D object recognition via geometric information preservation.Pattern Recognition,92,135-145.
MLA Liu HS,et al."Efficient 3D object recognition via geometric information preservation".Pattern Recognition 92(2019):135-145.
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