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An improved spatial point cloud simplification algorithm

  • S.I.: Machine Learning based semantic representation and analytics for multimedia application
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Abstract

Conventional data simplification algorithms depend much on scanning technology. However, with the development of the scanning technology, the conventional algorithm is unable to process numerous redundant data, leading to increased noise of point cloud data, failure of locating split center, and low simplification efficiency. In order to remove the noise in point cloud data, improve randomness for choosing the segmentation center, and obtain robust curvature information of the point cloud data and accurate segmentation results, the following improvements are made to the conventional spatial segmentation algorithms in this paper. Firstly, on the basis of denoising by B-spline wavelet filtering, voxel grid of point cloud data is carried out to select the effective segmentation center. Secondly, through the improvement of the subspace clustering algorithm, reasonable space division can be realized. Thirdly, based on the first-order neighbourhood surface of the vertices of the mesh surface, the curvature of sample points can be estimated. Fourthly, by introducing the distance between the neighbourhood sample and the target sample, the distance between the two points is taken as the geodesic distance, the smooth correction of the estimated result can be realized and the finally simplified point cloud segmentation result can be obtained. Finally, a robust spatial data simplification method is implemented. The experimental results show that this method can further segment spatial data and obtain simplified segmentation results while ensuring segmentation efficiency. Compared with the unified sampling method, the curvature simplification method, the isometric sampling method, and the random sampling method, the proposed method can reduce the sensitivity to boundary noise data, solve the problem of fuzzy boundary division, and overcome the disadvantage of poor accuracy caused by too high dimension.

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Acknowledgments

The research is supported by: Hebei Province Key Research and Development Project (No.20313701D); Hebei Province Key Research and Development Project (No.19210404D); National Natural Science Foundation of China (No. U1536112); National Social Science; Foundation Key Project (No. 17AJL014); National Natural Science Foundation of China (No. 81673697); National Natural Science Foundation of China (No. 61872046); "Blue Fire Project" (Huizhou) University of Technology Joint Innovation Project (CXZJHZ201729); Industry-University Cooperation Cooperative Education Project of the Ministry of Education (No. 201902218004); Industry-University Cooperation Cooperative Education Project of the Ministry of Education (No. 201902024006); The Ministry of Education Industry-University Cooperation Collaborative Education Project (No. 201901197001); Industry-University Cooperation Cooperative Education Project of the Ministry of Education (No. 201901197007); Industry-University Cooperation Collaborative Education Project of the Ministry of Education (No. 201901199005); Educational Reform Project of Beijing University of Posts and Telecommunications (No. 500520096,No: 500519813 No: 500521171,); Special project for youth research and innovation: Beijing University of Posts and Telecommunications Project on Tuberculosis (2019 PTB-011); Supported by the Fundamental Research Funds for the Central Universities (Grant No.2019RC52).

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Authors and Affiliations

  1. School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yi Sun

  2. International School, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Shenhu Zhang

  3. School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Tianqi Wang

  4. Institute of Quantitative & Technical Economics, Chinese Academy of Social Sciences, Beijing, 100028, China

    Feng Lou

  5. Academy of Science and Technology, Xianhe Environmental Protection Technology Joint Stock Limited Company, Hebei, 050035, China

    Jingjin Ma & Chunying Wang

  6. College of Information Engineering, Qingdao Qiushi College, Shandong, 266108, China

    Chengrong Gui

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  1. Yi Sun
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  4. Feng Lou
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Correspondence to Yi Sun.

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Conflicts of Interest

Compared with traditional segmentation or simplification algorithms, the proposed method not only reduces the sensitivity to noise data and improves the accuracy but also solves the defect of fuzzy boundary division, further overcomes the defect of inaccurate curvature weighting of target sample points, and achieves the smooth transition target of curvature of sample points.

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Sun, Y., Zhang, S., Wang, T. et al. An improved spatial point cloud simplification algorithm. Neural Comput & Applic 34, 12345–12359 (2022). https://doi.org/10.1007/s00521-021-06582-7

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