Automated Matching of Segmented Point Clouds to As-built Plans
Terrestrial laser scanning (TLS) is seeing an increase use for surveying and engineering applications. As such, there is much on-going research into automating the process for segmentation and feature extraction. This paper presents a simple method for segmenting the interior of a building and compa...
| Main Authors: | , , , |
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| Format: | Conference Paper |
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New Zealand Institute of Surveyors and the Surveying and Spatial Sciences Institute
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/16948 |
| _version_ | 1848749322933370880 |
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| author | Belton, David Mooney, Brian Snow, Anthony Bae, Kwang-Ho |
| author_facet | Belton, David Mooney, Brian Snow, Anthony Bae, Kwang-Ho |
| author_sort | Belton, David |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Terrestrial laser scanning (TLS) is seeing an increase use for surveying and engineering applications. As such, there is much on-going research into automating the process for segmentation and feature extraction. This paper presents a simple method for segmenting the interior of a building and comparing it to as-built plans. The method is based on analysing the local point attributes such as curvature, surface normal direction and underlying geometric structure. Random sampling consensus (RANSAC), region growing and voting techniques are applied to identify the predominant salient surface feature to extract wall and vertical segments. This information is used to generate a 2D plan of the interior space. A distance weighted method then automatically locates the corresponding vertices between the different datasets to transform them into a common coordinate system.A traditional survey was performed alongside the 3D point cloudcapture to compare and validate the generated 2D plans and the comparison to the existingdrawings. The accuracy of such generated plans from 3D point clouds will be explored. |
| first_indexed | 2025-11-14T07:19:06Z |
| format | Conference Paper |
| id | curtin-20.500.11937-16948 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:19:06Z |
| publishDate | 2011 |
| publisher | New Zealand Institute of Surveyors and the Surveying and Spatial Sciences Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-169482017-05-30T07:58:30Z Automated Matching of Segmented Point Clouds to As-built Plans Belton, David Mooney, Brian Snow, Anthony Bae, Kwang-Ho Terrestrial laser scanning (TLS) is seeing an increase use for surveying and engineering applications. As such, there is much on-going research into automating the process for segmentation and feature extraction. This paper presents a simple method for segmenting the interior of a building and comparing it to as-built plans. The method is based on analysing the local point attributes such as curvature, surface normal direction and underlying geometric structure. Random sampling consensus (RANSAC), region growing and voting techniques are applied to identify the predominant salient surface feature to extract wall and vertical segments. This information is used to generate a 2D plan of the interior space. A distance weighted method then automatically locates the corresponding vertices between the different datasets to transform them into a common coordinate system.A traditional survey was performed alongside the 3D point cloudcapture to compare and validate the generated 2D plans and the comparison to the existingdrawings. The accuracy of such generated plans from 3D point clouds will be explored. 2011 Conference Paper http://hdl.handle.net/20.500.11937/16948 New Zealand Institute of Surveyors and the Surveying and Spatial Sciences Institute fulltext |
| spellingShingle | Belton, David Mooney, Brian Snow, Anthony Bae, Kwang-Ho Automated Matching of Segmented Point Clouds to As-built Plans |
| title | Automated Matching of Segmented Point Clouds to As-built Plans |
| title_full | Automated Matching of Segmented Point Clouds to As-built Plans |
| title_fullStr | Automated Matching of Segmented Point Clouds to As-built Plans |
| title_full_unstemmed | Automated Matching of Segmented Point Clouds to As-built Plans |
| title_short | Automated Matching of Segmented Point Clouds to As-built Plans |
| title_sort | automated matching of segmented point clouds to as-built plans |
| url | http://hdl.handle.net/20.500.11937/16948 |