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Partial point cloud registration algorithm based on deep learning and non-corresponding point estimation

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Abstract

For the limitations of global feature-based deep learning point cloud registration algorithms in partial point cloud registration, this paper proposes a partial point cloud registration algorithm NcPE-PNLK combining global features and correspondence. The NcPE-PNLK algorithm introduces a feature interaction module to complete the information interaction between two point clouds in the feature extraction stage, which can improve the credibility of the feature. Moreover, the algorithm predicts the correspondence through the non-corresponding point estimation module, which reduces the influence of non-overlapping regions on the global feature and effectively solves the problem of performance degradation of the global feature registration algorithms in partial point cloud registration. We test the registration performance of NcPE-PNLK on synthetic scene dataset and real dataset in this paper. The experimental results show that NcPE-PNLK can effectively reduce the impact of non-overlapping regions in the registration process and achieve better performance compared with the global feature-based registration algorithms. In addition, compared with the correspondence-based registration algorithms, the NcPE-PNLK algorithm does not need to calculate correspondence precisely, which can achieve high-precision partial point cloud registration with guaranteed efficiency.

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Data availability

The experimental data that support the findings of this study are available in the Princeton ModelNet project with the identifier https://modelnet.cs.princeton.edu and the Stanford 3D Scanning Repository with the identifier https://graphics.stanford.edu/data/3Dscanrep

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Funding

This study was supported by the Science and Technology Project of Hebei Education Department and Tianjin Research Program of Application Foundation and Advanced Technology of China under Grant numbers ZD2018045 and 15JCYBJC17100.

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Authors and Affiliations

  1. School of Electronic Information Engineering, Hebei University of Technology, Tianjin, China

    Shenyi Wang, Zhilong Kang, Yanju Guo, Yuchen Zhao & Yuanfei Chai

  2. School of Information Engineering, Tianjin University of Commerce, Tianjin, China

    Lei Chen

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  1. Shenyi Wang
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  2. Zhilong Kang
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  4. Yanju Guo
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  5. Yuchen Zhao
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Contributions

SW developed the idea, implemented the code, and wrote the first draft of the manuscript. YZ and YC assisted with the experiment. All authors commented on previous versions of the manuscript and approved the final manuscript.

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Correspondence to Yanju Guo.

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Wang, S., Kang, Z., Chen, L. et al. Partial point cloud registration algorithm based on deep learning and non-corresponding point estimation. Vis Comput (2023). https://doi.org/10.1007/s00371-023-03103-6

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