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Proceedings Paper

LSAH: a fast and efficient local surface feature for point cloud registration
Author(s): Rongrong Lu; Feng Zhu; Qingxiao Wu; Yanzi Kong
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Paper Abstract

Point cloud registration is a fundamental task in high level three dimensional applications. Noise, uneven point density and varying point cloud resolutions are the three main challenges for point cloud registration. In this paper, we design a robust and compact local surface descriptor called Local Surface Angles Histogram (LSAH) and propose an effectively coarse to fine algorithm for point cloud registration. The LSAH descriptor is formed by concatenating five normalized sub-histograms into one histogram. The five sub-histograms are created by accumulating a different type of angle from a local surface patch respectively. The experimental results show that our LSAH is more robust to uneven point density and point cloud resolutions than four state-of-the-art local descriptors in terms of feature matching. Moreover, we tested our LSAH based coarse to fine algorithm for point cloud registration. The experimental results demonstrate that our algorithm is robust and efficient as well.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106151G (10 April 2018); doi: 10.1117/12.2303809
Show Author Affiliations
Rongrong Lu, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Labs of Opto-Electronic Information Processing/Image Understanding and Computer Vision (China)
Feng Zhu, Shenyang Institute of Automation (China)
Key Lab. of Opto-Electronic Information Processing (China)
Key Lab. of Image Understanding and Computer Vision (China)
Qingxiao Wu, Shenyang Institute of Automation (China)
Key Lab. of Opto-Electronic Information Processing (China)
Key Lab. of Image Understanding and Computer Vision (China)
Yanzi Kong, Shenyang Institute of Automation (China)
Univ. of Chinese Academy of Sciences (China)
Key Labs of Opto-Electronic Information Processing/Image Understanding and Computer Vision (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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