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An Extensible Local Surface Descriptor for 3D Object Recognition
Lu RR(鲁荣荣)1,2,3,4; Zhu F(朱枫)1,3,4; Hao YM(郝颖明)1,2,3,4; Cai HY(蔡慧英)1,2,3,4; Wu QX(吴清潇)1,3,4
Department光电信息技术研究室
Conference Name7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
Conference DateJuly 31 - August 4, 2017
Conference PlaceHawaii, USA
Author of SourceIEEE Robotics and Automation Society
Source Publication2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
PublisherIEEE
Publication PlaceNew York
2017
Pages611-616
Indexed ByEI ; CPCI(ISTP)
EI Accession number20183905873624
WOS IDWOS:000447628700112
Contribution Rank1
ISBN978-1-5386-0489-2
Abstract

This paper presents a novel local surface descriptor by encoding the neighboring points' position angles of a key point into a histogram. The generation of the feature descriptor is simple and efficient. Firstly, we construct a Local Reference Frame (LRF) by performing eigenvalue decomposition on a scatter covariance matrix. Then, the sphere support of the key point is divided into several sphere shells. In each sphere shell, we calculate the angles between a neighboring point and z-axis, x-axis respectively. Subsequently, the cosine values of these two angles are mapped into two 1D histograms respectively. Finally, all the 1D histograms are put together followed by a normalization to form the descriptor. Our proposed local surface descriptor is called Signature of Position Angles Histograms (SPAH). As for a point cloud with color information, the SPAH can easily be extended to a Color SPAH (CSPAH) descriptor only by adding one more 1D histogram generated by the color information in each sphere shell. The performance of the proposed SPAH was tested on the Bologna Dataset 1 to compare with several state-of-the-art feature descriptors. The experiment results show that our SPAH descriptor is more robust to noise and vary mesh decimations. Moreover, our SPAH and CSPAH descriptors based 3D object recognition algorithms achieved a good performance on the Bologna Dataset 3.

Language英语
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/22829
Collection光电信息技术研究室
Corresponding AuthorZhu F(朱枫)
Affiliation1.Shenyang Institute of Automation, CAS, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang 110016, China
4.Key Lab of Image Understanding and Computer Vision, Liaoning Province, Shenyang 110016, China
Recommended Citation
GB/T 7714
Lu RR,Zhu F,Hao YM,et al. An Extensible Local Surface Descriptor for 3D Object Recognition[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:611-616.
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