Robust segmentation in laser scanning 3D point cloud data
Segmentation is a most important intermediate step in point cloud data processing and understanding. Covariance statistics based local saliency features from Principal Component Analysis (PCA) are frequently used for point cloud segmentation. However it is well known that PCA is sensitive to outlier...
| Main Authors: | , , |
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| Format: | Conference Paper |
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IEEE eXpress Conference Publishing
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/16069 |
| _version_ | 1848749067653349376 |
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| author | Nurunnabi, Abdul Belton, David West, Geoffrey |
| author2 | - |
| author_facet | - Nurunnabi, Abdul Belton, David West, Geoffrey |
| author_sort | Nurunnabi, Abdul |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Segmentation is a most important intermediate step in point cloud data processing and understanding. Covariance statistics based local saliency features from Principal Component Analysis (PCA) are frequently used for point cloud segmentation. However it is well known that PCA is sensitive to outliers. Hence segmentation results can be erroneous and unreliable. The problems of surface segmentation in laser scanning point cloud data are investigated in this paper. We propose a region growing based statistically robust segmentation algorithm that uses a recently introduced fast Minimum Covariance Determinant (MCD) based robust PCA approach. Experiments for several real laser scanning datasets show that PCA gives unreliable and non-robust results whereas the proposed robust PCA based method has intrinsic ability to deal with noisy data and gives more accurate and robust results for planar and non planar smooth surface segmentation. |
| first_indexed | 2025-11-14T07:15:03Z |
| format | Conference Paper |
| id | curtin-20.500.11937-16069 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:15:03Z |
| publishDate | 2012 |
| publisher | IEEE eXpress Conference Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-160692017-11-02T05:47:45Z Robust segmentation in laser scanning 3D point cloud data Nurunnabi, Abdul Belton, David West, Geoffrey - feature extraction robust statistics robust normal region growing outlier covariance technique Segmentation is a most important intermediate step in point cloud data processing and understanding. Covariance statistics based local saliency features from Principal Component Analysis (PCA) are frequently used for point cloud segmentation. However it is well known that PCA is sensitive to outliers. Hence segmentation results can be erroneous and unreliable. The problems of surface segmentation in laser scanning point cloud data are investigated in this paper. We propose a region growing based statistically robust segmentation algorithm that uses a recently introduced fast Minimum Covariance Determinant (MCD) based robust PCA approach. Experiments for several real laser scanning datasets show that PCA gives unreliable and non-robust results whereas the proposed robust PCA based method has intrinsic ability to deal with noisy data and gives more accurate and robust results for planar and non planar smooth surface segmentation. 2012 Conference Paper http://hdl.handle.net/20.500.11937/16069 10.1109/DICTA.2012.6411672 IEEE eXpress Conference Publishing fulltext |
| spellingShingle | feature extraction robust statistics robust normal region growing outlier covariance technique Nurunnabi, Abdul Belton, David West, Geoffrey Robust segmentation in laser scanning 3D point cloud data |
| title | Robust segmentation in laser scanning 3D point cloud data |
| title_full | Robust segmentation in laser scanning 3D point cloud data |
| title_fullStr | Robust segmentation in laser scanning 3D point cloud data |
| title_full_unstemmed | Robust segmentation in laser scanning 3D point cloud data |
| title_short | Robust segmentation in laser scanning 3D point cloud data |
| title_sort | robust segmentation in laser scanning 3d point cloud data |
| topic | feature extraction robust statistics robust normal region growing outlier covariance technique |
| url | http://hdl.handle.net/20.500.11937/16069 |