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A novel approach to de-noising 3D point clouds using mean-shift based clustering algorithm
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Sahar Kamalou   , Mohammad javad Valadan zoej * , Ali Hosseini naveh , Fahime Youssefi |
| K. N. Toosi University of Technology |
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Abstract: (1739 Views) |
| Raw point clouds usually include noise and outliers. Also, the point clouds generated by photogrammetry methods are noisier than the point clouds that are derived from active methods such as laser scanners, hence many challenges for reconstructing and meshing surface using these three-dimensional data would be possible. Also, maintaining sharp features is essential during the process of noise removal. Many techniques have been developed to remove noise from the point cloud, but only a few of them are suitable for maintaining Sharp features during the noise removal process. This paper tries to provide a new statistical method with the ability to maintain sharp features, to remove noise. In the proposed method, first, the point cloud is clustered using the mean-shift clustering algorithm. As the clustering accuracy depends on the kernel size, the optimal size of the window is achieved through the hill climbing optimization. Then, in each cluster, the distance between each point and the mean of the other points of that cluster is calculated; next, appropriate thresholds are used to detect and remove noise from point cloud by applying them on the number of members of each cluster and computed distances. So the sharp features, such as the edges, are preserved. The experimental results obtained from the implementation of the proposed method on the three sets of 3D data ,provided by the laser scanner, illustrate that this method ,compared with the other methods presented in the literature review, increases the accuracy about 4% in noise removing and 5.19 percent in maintaining sharp features. |
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| Keywords: point cloud, noise removal, sharp feature, clustering, Optimization, threshold |
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Full-Text [PDF 1387 kb]
(568 Downloads)
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Type of Study: Research |
Subject:
Aerial Photogrammetry
Received: 2018/12/8 | Accepted: 2019/02/16 | ePublished ahead of print: 2022/01/1 | Published: 2022/03/7
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