Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical Manifold
A denoising algorithm was proposed for point cloud with high-density noise. The algorithm utilized geometric metrics on the statistical manifold and applied the idea of clustering K-means based on local statistical characteristics between noise and valid data. First, by calculating the expectation a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/14/8264 |
| _version_ | 1797590473466970112 |
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| author | Xiaomin Duan Li Feng Xinyu Zhao |
| author_facet | Xiaomin Duan Li Feng Xinyu Zhao |
| author_sort | Xiaomin Duan |
| collection | DOAJ |
| description | A denoising algorithm was proposed for point cloud with high-density noise. The algorithm utilized geometric metrics on the statistical manifold and applied the idea of clustering K-means based on local statistical characteristics between noise and valid data. First, by calculating the expectation and covariance matrix of the data points, the point cloud with high-density noise was projected onto the Gaussian distribution family manifold, aiming to form the parameter point cloud. Afterwards, the geometry metrics were assigned to the manifold, and the K-means algorithm was applied to cluster the parameter point cloud, so as to classify the valid data and noise. Furthermore, in order to analyze the robustness of the means with different metrics, the approximate values of their influence functions were calculated, respectively. Finally, simulation analysis was conducted using the algorithm based on geometric metrics to verify the effectiveness in point cloud denoising. |
| first_indexed | 2024-03-11T01:21:00Z |
| format | Article |
| id | doaj.art-c9312aec66db4756af557965ca43994d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:21:00Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c9312aec66db4756af557965ca43994d2023-11-18T18:10:40ZengMDPI AGApplied Sciences2076-34172023-07-011314826410.3390/app13148264Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical ManifoldXiaomin Duan0Li Feng1Xinyu Zhao2School of Science, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Science, Dalian Jiaotong University, Dalian 116028, ChinaSchool of Materials Science and Engineering, Dalian Jiaotong University, Dalian 116028, ChinaA denoising algorithm was proposed for point cloud with high-density noise. The algorithm utilized geometric metrics on the statistical manifold and applied the idea of clustering K-means based on local statistical characteristics between noise and valid data. First, by calculating the expectation and covariance matrix of the data points, the point cloud with high-density noise was projected onto the Gaussian distribution family manifold, aiming to form the parameter point cloud. Afterwards, the geometry metrics were assigned to the manifold, and the K-means algorithm was applied to cluster the parameter point cloud, so as to classify the valid data and noise. Furthermore, in order to analyze the robustness of the means with different metrics, the approximate values of their influence functions were calculated, respectively. Finally, simulation analysis was conducted using the algorithm based on geometric metrics to verify the effectiveness in point cloud denoising.https://www.mdpi.com/2076-3417/13/14/8264point cloud denoisingstatistical manifolddifferential geometryinfluence function |
| spellingShingle | Xiaomin Duan Li Feng Xinyu Zhao Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical Manifold Applied Sciences point cloud denoising statistical manifold differential geometry influence function |
| title | Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical Manifold |
| title_full | Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical Manifold |
| title_fullStr | Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical Manifold |
| title_full_unstemmed | Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical Manifold |
| title_short | Point Cloud Denoising Algorithm via Geometric Metrics on the Statistical Manifold |
| title_sort | point cloud denoising algorithm via geometric metrics on the statistical manifold |
| topic | point cloud denoising statistical manifold differential geometry influence function |
| url | https://www.mdpi.com/2076-3417/13/14/8264 |
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