Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data
Existing approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected w...
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| Format: | Article |
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MDPI AG
2021-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/8/1520 |
| _version_ | 1797412173396312064 |
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| author | Emon Kumar Dey Fayez Tarsha Kurdi Mohammad Awrangjeb Bela Stantic |
| author_facet | Emon Kumar Dey Fayez Tarsha Kurdi Mohammad Awrangjeb Bela Stantic |
| author_sort | Emon Kumar Dey |
| collection | DOAJ |
| description | Existing approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected without considering the geometric structure of the object and the distribution of the input point cloud. Thus, considering the object structure and the heterogeneous distribution of the point cloud, this paper proposes a new effective approach for selecting a minimal neighbourhood, which can vary for each input point. For each point, a minimal number of neighbouring points are iteratively selected. At each iteration, based on the calculated standard deviation from a fitted 3D line to the selected points, a decision is made adaptively about the neighbourhood. The selected minimal neighbouring points make the calculation of the normal vector accurate. The direction of the normal vector is then used to calculate the inside fold feature points. In addition, the Euclidean distance from a point to the calculated mean of its neighbouring points is used to make a decision about the boundary point. In the context of the accuracy evaluation, the experimental results confirm the competitive performance of the proposed approach of neighbourhood selection over the state-of-the-art methods. Based on our generated ground truth data, the proposed fold and boundary point extraction techniques show more than 90% F1-scores. |
| first_indexed | 2024-03-09T04:58:16Z |
| format | Article |
| id | doaj.art-8e1cc9e7f70c44eb90a837e22fb2790a |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T04:58:16Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-8e1cc9e7f70c44eb90a837e22fb2790a2023-12-03T13:03:34ZengMDPI AGRemote Sensing2072-42922021-04-01138152010.3390/rs13081520Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud DataEmon Kumar Dey0Fayez Tarsha Kurdi1Mohammad Awrangjeb2Bela Stantic3School of Information and Communication Technology, Griffith University, Nathan, QLD 4111, AustraliaSchool of Information and Communication Technology, Griffith University, Nathan, QLD 4111, AustraliaSchool of Information and Communication Technology, Griffith University, Nathan, QLD 4111, AustraliaSchool of Information and Communication Technology, Griffith University, Nathan, QLD 4111, AustraliaExisting approaches that extract buildings from point cloud data do not select the appropriate neighbourhood for estimation of normals on individual points. However, the success of these approaches depends on correct estimation of the normal vector. In most cases, a fixed neighbourhood is selected without considering the geometric structure of the object and the distribution of the input point cloud. Thus, considering the object structure and the heterogeneous distribution of the point cloud, this paper proposes a new effective approach for selecting a minimal neighbourhood, which can vary for each input point. For each point, a minimal number of neighbouring points are iteratively selected. At each iteration, based on the calculated standard deviation from a fitted 3D line to the selected points, a decision is made adaptively about the neighbourhood. The selected minimal neighbouring points make the calculation of the normal vector accurate. The direction of the normal vector is then used to calculate the inside fold feature points. In addition, the Euclidean distance from a point to the calculated mean of its neighbouring points is used to make a decision about the boundary point. In the context of the accuracy evaluation, the experimental results confirm the competitive performance of the proposed approach of neighbourhood selection over the state-of-the-art methods. Based on our generated ground truth data, the proposed fold and boundary point extraction techniques show more than 90% F1-scores.https://www.mdpi.com/2072-4292/13/8/1520boundary point extractionbuilding reconstructionedge pointfeature point extractionneighbourhood selection |
| spellingShingle | Emon Kumar Dey Fayez Tarsha Kurdi Mohammad Awrangjeb Bela Stantic Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data Remote Sensing boundary point extraction building reconstruction edge point feature point extraction neighbourhood selection |
| title | Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data |
| title_full | Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data |
| title_fullStr | Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data |
| title_full_unstemmed | Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data |
| title_short | Effective Selection of Variable Point Neighbourhood for Feature Point Extraction from Aerial Building Point Cloud Data |
| title_sort | effective selection of variable point neighbourhood for feature point extraction from aerial building point cloud data |
| topic | boundary point extraction building reconstruction edge point feature point extraction neighbourhood selection |
| url | https://www.mdpi.com/2072-4292/13/8/1520 |
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