FPFH-based graph matching for 3D point cloud registration

Jiapeng Zhao; Chen Li; Lihua Tian; Jihua Zhu

doi:10.1117/12.2309462

13 April 2018 FPFH-based graph matching for 3D point cloud registration

Jiapeng Zhao, Chen Li, Lihua Tian, Jihua Zhu

Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106960M (2018) https://doi.org/10.1117/12.2309462
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria

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.

Citation Download Citation

Jiapeng Zhao, Chen Li, Lihua Tian, and Jihua Zhu "FPFH-based graph matching for 3D point cloud registration", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106960M (13 April 2018); https://doi.org/10.1117/12.2309462

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