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Proceedings Paper

FPFH-based graph matching for 3D point cloud registration
Author(s): Jiapeng Zhao; Chen Li; Lihua Tian; Jihua Zhu
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Paper Abstract

Correspondence detection is a vital step in point cloud registration and it can help getting a reliable initial alignment. In this paper, we put forward an advanced point feature-based graph matching algorithm to solve the initial alignment problem of rigid 3D point cloud registration with partial overlap. Specifically, Fast Point Feature Histograms are used to determine the initial possible correspondences firstly. Next, a new objective function is provided to make the graph matching more suitable for partially overlapping point cloud. The objective function is optimized by the simulated annealing algorithm for final group of correct correspondences. Finally, we present a novel set partitioning method which can transform the NP-hard optimization problem into a O(n3)-solvable one. Experiments on the Stanford and UWA public data sets indicates that our method can obtain better result in terms of both accuracy and time cost compared with other point cloud registration methods.

Paper Details

Date Published: 13 April 2018
PDF: 13 pages
Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960M (13 April 2018); doi: 10.1117/12.2309462
Show Author Affiliations
Jiapeng Zhao, Xi'an Jiaotong Univ. (China)
Chen Li, Xi'an Jiaotong Univ. (China)
Lihua Tian, Xi'an Jiaotong Univ. (China)
Jihua Zhu, Xi'an Jiaotong Univ. (China)

Published in SPIE Proceedings Vol. 10696:
Tenth International Conference on Machine Vision (ICMV 2017)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev; Jianhong Zhou, Editor(s)

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