Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information
This paper proposes a single-stage adaptive multi-scale noise filtering algorithm for point clouds, based on feature information, which aims to mitigate the fact that the current laser point cloud noise filtering algorithm has difficulty quickly completing the single-stage adaptive filtering of mult...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/2/367 |
| _version_ | 1797490678702276608 |
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| author | Zhen Zheng Bingting Zha Yu Zhou Jinbo Huang Youshi Xuchen He Zhang |
| author_facet | Zhen Zheng Bingting Zha Yu Zhou Jinbo Huang Youshi Xuchen He Zhang |
| author_sort | Zhen Zheng |
| collection | DOAJ |
| description | This paper proposes a single-stage adaptive multi-scale noise filtering algorithm for point clouds, based on feature information, which aims to mitigate the fact that the current laser point cloud noise filtering algorithm has difficulty quickly completing the single-stage adaptive filtering of multi-scale noise. The feature information from each point of the point cloud is obtained based on the efficient k-dimensional (k-d) tree data structure and amended normal vector estimation methods, and the adaptive threshold is used to divide the point cloud into large-scale noise, a feature-rich region, and a flat region to reduce the computational time. The large-scale noise is removed directly, the feature-rich and flat regions are filtered via improved bilateral filtering algorithm and weighted average filtering algorithm based on grey relational analysis, respectively. Simulation results show that the proposed algorithm performs better than the state-of-art comparison algorithms. It was, thus, verified that the algorithm proposed in this paper can quickly and adaptively (i) filter out large-scale noise, (ii) smooth small-scale noise, and (iii) effectively maintain the geometric features of the point cloud. The developed algorithm provides research thought for filtering pre-processing methods applicable in 3D measurements, remote sensing, and target recognition based on point clouds. |
| first_indexed | 2024-03-10T00:36:27Z |
| format | Article |
| id | doaj.art-7aa5dc38ad2d4525a2d926026ebebbce |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T00:36:27Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7aa5dc38ad2d4525a2d926026ebebbce2023-11-23T15:16:32ZengMDPI AGRemote Sensing2072-42922022-01-0114236710.3390/rs14020367Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature InformationZhen Zheng0Bingting Zha1Yu Zhou2Jinbo Huang3Youshi Xuchen4He Zhang5ZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094, ChinaZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094, ChinaZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094, ChinaZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094, ChinaZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094, ChinaZNDY of Ministerial Key Laboratory, Nanjing University of Science and Technology, Nanjing 210094, ChinaThis paper proposes a single-stage adaptive multi-scale noise filtering algorithm for point clouds, based on feature information, which aims to mitigate the fact that the current laser point cloud noise filtering algorithm has difficulty quickly completing the single-stage adaptive filtering of multi-scale noise. The feature information from each point of the point cloud is obtained based on the efficient k-dimensional (k-d) tree data structure and amended normal vector estimation methods, and the adaptive threshold is used to divide the point cloud into large-scale noise, a feature-rich region, and a flat region to reduce the computational time. The large-scale noise is removed directly, the feature-rich and flat regions are filtered via improved bilateral filtering algorithm and weighted average filtering algorithm based on grey relational analysis, respectively. Simulation results show that the proposed algorithm performs better than the state-of-art comparison algorithms. It was, thus, verified that the algorithm proposed in this paper can quickly and adaptively (i) filter out large-scale noise, (ii) smooth small-scale noise, and (iii) effectively maintain the geometric features of the point cloud. The developed algorithm provides research thought for filtering pre-processing methods applicable in 3D measurements, remote sensing, and target recognition based on point clouds.https://www.mdpi.com/2072-4292/14/2/367point cloud denoisingkd-treegrey relational analysisprincipal component analysisadaptive thresholdbilateral filtering algorithm |
| spellingShingle | Zhen Zheng Bingting Zha Yu Zhou Jinbo Huang Youshi Xuchen He Zhang Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information Remote Sensing point cloud denoising kd-tree grey relational analysis principal component analysis adaptive threshold bilateral filtering algorithm |
| title | Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information |
| title_full | Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information |
| title_fullStr | Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information |
| title_full_unstemmed | Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information |
| title_short | Single-Stage Adaptive Multi-Scale Point Cloud Noise Filtering Algorithm Based on Feature Information |
| title_sort | single stage adaptive multi scale point cloud noise filtering algorithm based on feature information |
| topic | point cloud denoising kd-tree grey relational analysis principal component analysis adaptive threshold bilateral filtering algorithm |
| url | https://www.mdpi.com/2072-4292/14/2/367 |
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