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Position-aware feature matching algorithm based non-rigid point cloud registration

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Abstract

Existing non-rigid point cloud registration algorithms often encounter matching errors arise when dealing with similar regions in point clouds, and it is difficult to promptly filter out these misaligned corresponding points. To address these issues, a non-rigid point cloud registration algorithm based on position-aware feature matching is proposed. Firstly, the relative positions of the point cloud are encoded using Fourier transform, decomposing the 3D point cloud of non-rigid objects into feature space information and 3D positional information. This enables non-rigid point cloud registration for similar parts while preventing the loss of positional information during network iterations. Secondly, global information is summarized through the self-attention layers of the transformation blocks, and point cloud information exchange is facilitated through cross-attention layers to promote feature matching between the source and target point clouds. Next, we design and integrate an outlier removal strategy into a high-dimensional convolutional neural network to eliminate incorrect matching correspondences. The Welsch function is applied in the regularization term of the loss function to enhance the algorithm’s robustness against noise and partially overlapping point clouds. Finally, comparative experiments with seven existing algorithms on the 4Dmatch/4Dlomatch dataset demonstrate that our proposed method outperforms the second-best algorithm by 3.3\(-\)6.3% in the correspondence index (IR) and 19.15\(-\)21.57% in the registration result index (Accr). The experimental results indicate that our method can effectively handle feature-similar regions in point clouds, promptly filter out misaligned corresponding points, and produce more accurate registration results, especially for lower overlap rates.

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Data availability

The data that support the findings of this study are available from the corresponding author, [RZ], upon reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant (52375178), in part by the Natural Science Foundation of Shanxi Province under Grant (202203021211206) and (202203021211189),and partially funded by Graduate Student Innovation Program of Taiyuan University of Science and Technology (SY2023039).

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Authors and Affiliations

  1. School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, Shanxi, China

    Ronqi Wang, Ronguo Zhang, Jing Hu, Rui Zhang & Lifang Wang

  2. School of Mechanical Engineering, Hefei University of Technology, Hefei, 230009, Anhui, China

    Xiaojun Liu

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  1. Ronqi Wang
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Correspondence to Ronguo Zhang.

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Communicated by Chenggang Yan.

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Wang, R., Zhang, R., Hu, J. et al. Position-aware feature matching algorithm based non-rigid point cloud registration. Multimedia Systems 31, 55 (2025). https://doi.org/10.1007/s00530-024-01657-6

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