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Local feature guidance framework for robust 3D point cloud registration

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Abstract

3D point cloud registration is a basic task in computer vision. In recent years, various of learning-based methods have been proposed to solve this problem. These methods effectively overcome the original problem of over-reliance on initial conditions and enhance the ability of obtaining the corresponding relationship. However, few methods pay enough attention to local features and tend to cause some mismatches. Therefore, this paper proposes two networks to extract local features sufficiently. To obtain a more accurate correspondence relationship between the point clouds, we propose a feature weight allocation network (FWANet), in which the expression ability of feature is enhanced using the proposed significant feature extraction module. Besides that, we utilize an interference elimination module to remove the interference points and enhance the internal correlation of point clouds. We also propose a spatial structural generation network (SSGNet), which fully utilizes the spatial location information to determine the spatial correspondence and generate a reliable connection after concatenating multi-dimensional features. At last, a complete feature space can be effectively captured after combining our FWANet with SSGNet together. We conducted multiple experiments on ModelNet40 datasets and achieved excellent results. Experimental results on four types of data demonstrate the superiority of our algorithm against the state-of-the-art ones. Our code will be available at https://github.com/liu-zikang/registration as soon as the paper is accepted.

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Funding

This study was supported by the National Natural Science Foundation of China (No. 62171314), and the recipient of the support was Kai He.

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, China

    Zikang Liu, Kai He, Dazhuang Zhang & Lei Wang

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  1. Zikang Liu
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Contributions

Zikang Liu and Kai He were involved in the conceptualization; Zikang Liu contributed to the methodology; Zikang Liu contributed to the software; Zikang Liu and Kai He assisted in the validation; Zikang Liu helped in the formal analysis; Dazhuang Zhang was involved in the investigation; Lei Wang contributed to the resources; Dazhuang Zhang contributed to the data curation; Lei Wang was involved in writing—original draft preparation; Kai He contributed to writing—review and editing and assisted in the supervision, project administration and funding acquisition. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Kai He.

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Liu, Z., He, K., Zhang, D. et al. Local feature guidance framework for robust 3D point cloud registration. Vis Comput 39, 6459–6472 (2023). https://doi.org/10.1007/s00371-022-02739-0

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