Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template

Version 1 : Received: 9 September 2021 / Approved: 10 September 2021 / Online: 10 September 2021 (10:26:10 CEST)

How to cite: Su, S.; Wang, C.; Chen, K.; Zhang, J.; Hui, Y. MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template. Preprints 2021, 2021090180 (doi: 10.20944/preprints202109.0180.v1). Su, S.; Wang, C.; Chen, K.; Zhang, J.; Hui, Y. MPCR-Net: Multiple Partial Point Clouds Registration Network Using a Global Template. Preprints 2021, 2021090180 (doi: 10.20944/preprints202109.0180.v1).

Abstract

With the advancement of photoelectric technology and computer image processing technology, the visual measurement method based on point clouds is gradually applied to the 3D measurement of large workpieces. Point cloud registration is a key step in 3D measurement, and its registration accuracy directly affects the accuracy of 3D measurements. In this study, we designed a novel MPCR-Net for multiple partial point cloud registration networks. First, an ideal point cloud was extracted from the CAD model of the workpiece and was used as the global template. Next, a deep neural network was used to search for the corresponding point groups between each partial point cloud and the global template point cloud. Then, the rigid body transformation matrix was learned according to these correspondence point groups to realize the registration of each partial point cloud. Finally, the iterative closest point algorithm was used to optimize the registration results to obtain a final point cloud model of the workpiece. We conducted point cloud registration experiments on untrained models and actual workpieces, and by comparing them with existing point cloud registration methods, we verified that the MPCR-Net could improve the accuracy and robustness of the 3D point cloud registration.

Keywords

point cloud registration; template point cloud; multiple partial point cloud; deep learning

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