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Automatic multi-view registration of point clouds via a high-quality descriptor and a novel 3D transformation estimation technique

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Abstract

Generally, performing multiple scans is necessary to cover entire scanning area, and multiple point clouds are thus obtained. These point clouds need to be registered together, which is the focus of our work. However, influenced by the noise, point density variation, partial overlap and so on, the success rate of registration is still low. For this reason, a pairwise registration method is first proposed. It includes two main components: the triple local coordinate image (TriLCI) descriptor and a new 3D transformation estimation technique. The descriptor has high descriptiveness and strong robustness, by which more right correspondences can be achieved. The 3D transformation estimation technique is employed to calculate the rotation matrix and translation vector under the condition that most of correspondences are false. In this technique, a new geometric distance constraint is developed, and is then applied to eliminate incorrect correspondences. The robust estimation based on the median is introduced to calculate the rotation matrix and translation vector. For multi-view registration, the connected graph algorithm and shape growing based method are applied. A comparative study is presented. The experimental results well demonstrate that the proposed pairwise registration method has high success rate. The comparative study gives the merits and demerits of the two multi-view registration methods.

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Data availability statements

The datasets generated during and/or analysed during the current study are available in the websites http://staffhome.ecm.uwa.edu.au/~00053650/recognition.html and http://redwood-data.org/indoor/regbasic.html.

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Acknowledgements

The authors would like to acknowledge the publishers of the UWA3M and ICL-NUIM datasets. This work is supported by the National Natural Science Foundation of China (41674005, 42104023), Jiangxi University of Science and Technology High-level Talent Research Startup Project (2021205200100564) and Spatial Cognition Augmented High-usability High-precision Smartphone Indoor Positioning (41874031).

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Authors and Affiliations

  1. School of Mathematics and Computer Sciences, Nanchang University, Nanchang, 330031, China

    Wuyong Tao

  2. School of Geomatics and Geodesy, Wuhan University, Wuhan, 430079, China

    Xianghong Hua

  3. School of Civil and Surveying and Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Xiaoxing He

  4. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Jingbin Liu & Dong Xu

  5. Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, 02430, Masala, Finland

    Jingbin Liu

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Tao, W., Hua, X., He, X. et al. Automatic multi-view registration of point clouds via a high-quality descriptor and a novel 3D transformation estimation technique. Vis Comput 40, 2615–2630 (2024). https://doi.org/10.1007/s00371-023-02942-7

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