Smoothing Algorithm of Point Cloud Based on Normal Vector Correction
The presence of outliers and noise points in the cloud data of the reverse engineering data collection directly affects the mult-view’s combination of the data，feature extraction，data reduction and the quality of surface reconstruction. Based on the research of bilateral filtering and trilateration...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2018-10-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1589 |
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| author | XUE Ping YAO Juan ZOU Xue-zhou WANG Hong-min |
| author_facet | XUE Ping YAO Juan ZOU Xue-zhou WANG Hong-min |
| author_sort | XUE Ping |
| collection | DOAJ |
| description | The presence of outliers and noise points in the cloud data of the reverse engineering data collection directly affects the mult-view’s combination of the data，feature extraction，data reduction and the quality of surface reconstruction. Based on the research of bilateral filtering and trilateration filtering algorithm，this paper presents an algorithm of denoising and smoothing of point cloud data based on normal vector correction. Firstly，the local neighborhood of the point cloud data is constructed，and the noise points of the scattered data collected by the data acquisition system are classified and processed. For outliers in the point cloud data，mathematical statistics analysis is used to filter out the points whose KNN is lower than the threshold. The points with similar geometric characteristics are restricted to the regions where the normal vectors are similar，and the normal vectors and positions of the samples in the similar neighborhoods are triangulated . The improved algorithm can effectively filter the outliers and noise points in the point cloud data，and ensure the sharp and edge features of the point cloud data and obtain good denoising effect. |
| format | Article |
| id | doaj-art-a75e218a29ce459c95d15cbb6d8ed3ec |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2018-10-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-a75e218a29ce459c95d15cbb6d8ed3ec2025-08-21T05:29:29ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832018-10-012305869110.15938/j.jhust.2018.05.015Smoothing Algorithm of Point Cloud Based on Normal Vector CorrectionXUE Ping0YAO Juan1ZOU Xue-zhou2WANG Hong-min3School of Automation，Harbin University of Science and Technology，Harbin 150080，ChinaSchool of Automation，Harbin University of Science and Technology，Harbin 150080，ChinaSchool of Automation，Harbin University of Science and Technology，Harbin 150080，ChinaSchool of Automation，Harbin University of Science and Technology，Harbin 150080，ChinaThe presence of outliers and noise points in the cloud data of the reverse engineering data collection directly affects the mult-view’s combination of the data，feature extraction，data reduction and the quality of surface reconstruction. Based on the research of bilateral filtering and trilateration filtering algorithm，this paper presents an algorithm of denoising and smoothing of point cloud data based on normal vector correction. Firstly，the local neighborhood of the point cloud data is constructed，and the noise points of the scattered data collected by the data acquisition system are classified and processed. For outliers in the point cloud data，mathematical statistics analysis is used to filter out the points whose KNN is lower than the threshold. The points with similar geometric characteristics are restricted to the regions where the normal vectors are similar，and the normal vectors and positions of the samples in the similar neighborhoods are triangulated . The improved algorithm can effectively filter the outliers and noise points in the point cloud data，and ensure the sharp and edge features of the point cloud data and obtain good denoising effect.https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1589point cloud datadenoising smoothingweighted covariance matrixtrilateration filteringnormal vector correction |
| spellingShingle | XUE Ping YAO Juan ZOU Xue-zhou WANG Hong-min Smoothing Algorithm of Point Cloud Based on Normal Vector Correction Journal of Harbin University of Science and Technology point cloud data denoising smoothing weighted covariance matrix trilateration filtering normal vector correction |
| title | Smoothing Algorithm of Point Cloud Based on Normal Vector Correction |
| title_full | Smoothing Algorithm of Point Cloud Based on Normal Vector Correction |
| title_fullStr | Smoothing Algorithm of Point Cloud Based on Normal Vector Correction |
| title_full_unstemmed | Smoothing Algorithm of Point Cloud Based on Normal Vector Correction |
| title_short | Smoothing Algorithm of Point Cloud Based on Normal Vector Correction |
| title_sort | smoothing algorithm of point cloud based on normal vector correction |
| topic | point cloud data denoising smoothing weighted covariance matrix trilateration filtering normal vector correction |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1589 |
| work_keys_str_mv | AT xueping smoothingalgorithmofpointcloudbasedonnormalvectorcorrection AT yaojuan smoothingalgorithmofpointcloudbasedonnormalvectorcorrection AT zouxuezhou smoothingalgorithmofpointcloudbasedonnormalvectorcorrection AT wanghongmin smoothingalgorithmofpointcloudbasedonnormalvectorcorrection |