Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data
This paper investigates the problems of outliers and/or noise in surface segmentation and proposes a statistically robust segmentation algorithm for laser scanning 3-D point cloud data. Principal component analysis (PCA)-based local saliency features, e.g., normal and curvature, have been frequently...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
IEEE Geoscience and Remote Sensing Society
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/63211 |
| _version_ | 1848761024642023424 |
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| author | Nurunnabi, Abdul Belton, David West, Geoff |
| author_facet | Nurunnabi, Abdul Belton, David West, Geoff |
| author_sort | Nurunnabi, Abdul |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper investigates the problems of outliers and/or noise in surface segmentation and proposes a statistically robust segmentation algorithm for laser scanning 3-D point cloud data. Principal component analysis (PCA)-based local saliency features, e.g., normal and curvature, have been frequently used in many ways for point cloud segmentation. However, PCA is sensitive to outliers; saliency features from PCA are nonrobust and inaccurate in the presence of outliers; consequently, segmentation results can be erroneous and unreliable. As a remedy, robust techniques, e.g., RANdom SAmple Consensus (RANSAC), and/or robust versions of PCA (RPCA) have been proposed. However, RANSAC is influenced by the well-known swamping effect, and RPCA methods are computationally intensive for point cloud processing. We propose a region growing based robust segmentation algorithm that uses a recently introduced maximum consistency with minimum distance based robust diagnostic PCA (RDPCA) approach to get robust saliency features. Experiments using synthetic and laser scanning data sets show that the RDPCA-based method has an intrinsic ability to deal with outlier-and/or noise-contaminated data. Results for a synthetic data set show that RDPCA is 105 times faster than RPCA and gives more accurate and robust results when compared with other segmentation methods. Compared with RANSAC and RPCA based methods, RDPCA takes almost the same time as RANSAC, but RANSAC results are markedly worse than RPCA and RDPCA results. Coupled with a segment merging algorithm, the proposed method is efficient for huge volumes of point cloud data consisting of complex objects surfaces from mobile, terrestrial, and aerial laser scanning systems. |
| first_indexed | 2025-11-14T10:25:06Z |
| format | Journal Article |
| id | curtin-20.500.11937-63211 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:25:06Z |
| publishDate | 2016 |
| publisher | IEEE Geoscience and Remote Sensing Society |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-632112018-05-16T07:04:56Z Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data Nurunnabi, Abdul Belton, David West, Geoff This paper investigates the problems of outliers and/or noise in surface segmentation and proposes a statistically robust segmentation algorithm for laser scanning 3-D point cloud data. Principal component analysis (PCA)-based local saliency features, e.g., normal and curvature, have been frequently used in many ways for point cloud segmentation. However, PCA is sensitive to outliers; saliency features from PCA are nonrobust and inaccurate in the presence of outliers; consequently, segmentation results can be erroneous and unreliable. As a remedy, robust techniques, e.g., RANdom SAmple Consensus (RANSAC), and/or robust versions of PCA (RPCA) have been proposed. However, RANSAC is influenced by the well-known swamping effect, and RPCA methods are computationally intensive for point cloud processing. We propose a region growing based robust segmentation algorithm that uses a recently introduced maximum consistency with minimum distance based robust diagnostic PCA (RDPCA) approach to get robust saliency features. Experiments using synthetic and laser scanning data sets show that the RDPCA-based method has an intrinsic ability to deal with outlier-and/or noise-contaminated data. Results for a synthetic data set show that RDPCA is 105 times faster than RPCA and gives more accurate and robust results when compared with other segmentation methods. Compared with RANSAC and RPCA based methods, RDPCA takes almost the same time as RANSAC, but RANSAC results are markedly worse than RPCA and RDPCA results. Coupled with a segment merging algorithm, the proposed method is efficient for huge volumes of point cloud data consisting of complex objects surfaces from mobile, terrestrial, and aerial laser scanning systems. 2016 Journal Article http://hdl.handle.net/20.500.11937/63211 10.1109/TGRS.2016.2551546 IEEE Geoscience and Remote Sensing Society restricted |
| spellingShingle | Nurunnabi, Abdul Belton, David West, Geoff Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data |
| title | Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data |
| title_full | Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data |
| title_fullStr | Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data |
| title_full_unstemmed | Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data |
| title_short | Robust Segmentation for Large Volumes of Laser Scanning Three-Dimensional Point Cloud Data |
| title_sort | robust segmentation for large volumes of laser scanning three-dimensional point cloud data |
| url | http://hdl.handle.net/20.500.11937/63211 |