Robust statistical approaches for local planar surface fitting in 3D laser scanning data
This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is kno...
| Main Authors: | , , |
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| Format: | Journal Article |
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Elsevier BV
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/30878 |
| _version_ | 1848753216112558080 |
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| author | Nurunnabi, A. Belton, David West, Geoff |
| author_facet | Nurunnabi, A. Belton, David West, Geoff |
| author_sort | Nurunnabi, A. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks.Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods. |
| first_indexed | 2025-11-14T08:20:59Z |
| format | Journal Article |
| id | curtin-20.500.11937-30878 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:20:59Z |
| publishDate | 2014 |
| publisher | Elsevier BV |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-308782017-09-13T15:55:20Z Robust statistical approaches for local planar surface fitting in 3D laser scanning data Nurunnabi, A. Belton, David West, Geoff Point cloud Robustness - Segmentation Plane fitting Surface reconstruction Normal estimation Feature extraction 3D modelling Outlier This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks.Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods. 2014 Journal Article http://hdl.handle.net/20.500.11937/30878 10.1016/j.isprsjprs.2014.07.004 Elsevier BV fulltext |
| spellingShingle | Point cloud Robustness - Segmentation Plane fitting Surface reconstruction Normal estimation Feature extraction 3D modelling Outlier Nurunnabi, A. Belton, David West, Geoff Robust statistical approaches for local planar surface fitting in 3D laser scanning data |
| title | Robust statistical approaches for local planar surface fitting in 3D laser scanning data |
| title_full | Robust statistical approaches for local planar surface fitting in 3D laser scanning data |
| title_fullStr | Robust statistical approaches for local planar surface fitting in 3D laser scanning data |
| title_full_unstemmed | Robust statistical approaches for local planar surface fitting in 3D laser scanning data |
| title_short | Robust statistical approaches for local planar surface fitting in 3D laser scanning data |
| title_sort | robust statistical approaches for local planar surface fitting in 3d laser scanning data |
| topic | Point cloud Robustness - Segmentation Plane fitting Surface reconstruction Normal estimation Feature extraction 3D modelling Outlier |
| url | http://hdl.handle.net/20.500.11937/30878 |