Robust Outlier Detection and Saliency Features Estimation in Point Cloud Data
This paper investigates outlier detection and reliable local saliency features (e.g. normal) estimation in point cloud data. We propose two highly robust outlier detection algorithms that are able to identify outliers and are efficient for reliable local saliency features estimation in noisy point c...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE Inc.
2013
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/29630 |
| _version_ | 1848752856419532800 |
|---|---|
| author | Nurunnabi, Abdul Belton, David West, Geoff |
| author2 | N/A |
| author_facet | N/A Nurunnabi, Abdul Belton, David West, Geoff |
| author_sort | Nurunnabi, Abdul |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper investigates outlier detection and reliable local saliency features (e.g. normal) estimation in point cloud data. We propose two highly robust outlier detection algorithms that are able to identify outliers and are efficient for reliable local saliency features estimation in noisy point cloud data. One is based on a univariate robust z-score and the other on a multivariate Mahalanobis type robust distance. They combine the ideas of orthogonal distance and local surface points consistency to get Maximum Consistency with Minimum Distance (MCMD). Experimental results are presented to show the algorithms' performance and are compared with other existing methods for synthetic and real datasets through segmentation for planar and non-planar surfaces of complex objects. The algorithms give more accurate and robust results, are fast and have the potential for local surface reconstruction, fitting, registration and covariance statistics based point cloud processing. |
| first_indexed | 2025-11-14T08:15:16Z |
| format | Conference Paper |
| id | curtin-20.500.11937-29630 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:15:16Z |
| publishDate | 2013 |
| publisher | IEEE Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-296302017-09-13T15:27:12Z Robust Outlier Detection and Saliency Features Estimation in Point Cloud Data Nurunnabi, Abdul Belton, David West, Geoff N/A saliency features plane fitting feature extraction surface reconstruction robust normal segmentation laser scanning robust curvature outlier This paper investigates outlier detection and reliable local saliency features (e.g. normal) estimation in point cloud data. We propose two highly robust outlier detection algorithms that are able to identify outliers and are efficient for reliable local saliency features estimation in noisy point cloud data. One is based on a univariate robust z-score and the other on a multivariate Mahalanobis type robust distance. They combine the ideas of orthogonal distance and local surface points consistency to get Maximum Consistency with Minimum Distance (MCMD). Experimental results are presented to show the algorithms' performance and are compared with other existing methods for synthetic and real datasets through segmentation for planar and non-planar surfaces of complex objects. The algorithms give more accurate and robust results, are fast and have the potential for local surface reconstruction, fitting, registration and covariance statistics based point cloud processing. 2013 Conference Paper http://hdl.handle.net/20.500.11937/29630 10.1109/CRV.2013.28 IEEE Inc. restricted |
| spellingShingle | saliency features plane fitting feature extraction surface reconstruction robust normal segmentation laser scanning robust curvature outlier Nurunnabi, Abdul Belton, David West, Geoff Robust Outlier Detection and Saliency Features Estimation in Point Cloud Data |
| title | Robust Outlier Detection and Saliency Features Estimation in Point Cloud Data |
| title_full | Robust Outlier Detection and Saliency Features Estimation in Point Cloud Data |
| title_fullStr | Robust Outlier Detection and Saliency Features Estimation in Point Cloud Data |
| title_full_unstemmed | Robust Outlier Detection and Saliency Features Estimation in Point Cloud Data |
| title_short | Robust Outlier Detection and Saliency Features Estimation in Point Cloud Data |
| title_sort | robust outlier detection and saliency features estimation in point cloud data |
| topic | saliency features plane fitting feature extraction surface reconstruction robust normal segmentation laser scanning robust curvature outlier |
| url | http://hdl.handle.net/20.500.11937/29630 |