Building Plane Segmentation Based on Point Clouds
Planes are essential features to describe the shapes of buildings. The segmentation of a plane is significant when reconstructing a building in three dimensions. However, there is a concern about the accuracy in segmenting plane from point cloud data. The objective of this paper was to develop an ef...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/1/95 |
| _version_ | 1797497783111909376 |
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| author | Zhonghua Su Zhenji Gao Guiyun Zhou Shihua Li Lihui Song Xukun Lu Ning Kang |
| author_facet | Zhonghua Su Zhenji Gao Guiyun Zhou Shihua Li Lihui Song Xukun Lu Ning Kang |
| author_sort | Zhonghua Su |
| collection | DOAJ |
| description | Planes are essential features to describe the shapes of buildings. The segmentation of a plane is significant when reconstructing a building in three dimensions. However, there is a concern about the accuracy in segmenting plane from point cloud data. The objective of this paper was to develop an effective segmentation algorithm for building planes that combines the region growing algorithm with the distance algorithm based on boundary points. The method was tested on point cloud data from a cottage and pantry as scanned using a Faro Focus 3D laser range scanner and Matterport Camera, respectively. A coarse extraction of the building plane was obtained from the region growing algorithm. The coplanar points where two planes intersect were obtained from the distance algorithm. The building plane’s optimal segmentation was then obtained by combining the coarse extraction plane points and the corresponding coplanar points. The results show that the proposed method successfully segmented the plane points of the cottage and pantry. The optimal distance thresholds using the proposed method from the uncoarse extraction plane points to each plane boundary point of cottage and pantry were 0.025 m and 0.030 m, respectively. The highest correct rate and the highest error rate of the cottage’s (pantry’s) plane segmentations using the proposed method under the optimal distance threshold were 99.93% and 2.30% (98.55% and 2.44%), respectively. The F1 score value of the cottage’s and pantry’s plane segmentations using the proposed method under the optimal distance threshold reached 97.56% and 95.75%, respectively. This method can segment different objects on the same plane, while the random sample consensus (RANSAC) algorithm causes the plane to become over-segmented. The proposed method can also extract the coplanar points at the intersection of two planes, which cannot be separated using the region growing algorithm. Although the RANSAC-RG method combining the RANSAC algorithm and the region growing algorithm can optimize the segmentation results of the RANSAC (region growing) algorithm and has little difference in segmentation effect (especially for cottage data) with the proposed method, the method still loses coplanar points at some intersection of the two planes. |
| first_indexed | 2024-03-10T03:24:11Z |
| format | Article |
| id | doaj.art-90f8208b31d8478b88c1c5b96a8ce0e7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:24:11Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-90f8208b31d8478b88c1c5b96a8ce0e72023-11-23T12:13:03ZengMDPI AGRemote Sensing2072-42922021-12-011419510.3390/rs14010095Building Plane Segmentation Based on Point CloudsZhonghua Su0Zhenji Gao1Guiyun Zhou2Shihua Li3Lihui Song4Xukun Lu5Ning Kang6School of Resources and Environment, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, ChinaTechnology Innovation Center of Geological Information of Ministry of Natural Resources, No. 45, Fuwai Street, Xicheng District, Beijing 100037, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, ChinaCollege of Electrical and Information Engineering, Hunan University, Lushan Road (S), Yuelu District, Changsha 410082, ChinaTechnology Innovation Center of Geological Information of Ministry of Natural Resources, No. 45, Fuwai Street, Xicheng District, Beijing 100037, ChinaPlanes are essential features to describe the shapes of buildings. The segmentation of a plane is significant when reconstructing a building in three dimensions. However, there is a concern about the accuracy in segmenting plane from point cloud data. The objective of this paper was to develop an effective segmentation algorithm for building planes that combines the region growing algorithm with the distance algorithm based on boundary points. The method was tested on point cloud data from a cottage and pantry as scanned using a Faro Focus 3D laser range scanner and Matterport Camera, respectively. A coarse extraction of the building plane was obtained from the region growing algorithm. The coplanar points where two planes intersect were obtained from the distance algorithm. The building plane’s optimal segmentation was then obtained by combining the coarse extraction plane points and the corresponding coplanar points. The results show that the proposed method successfully segmented the plane points of the cottage and pantry. The optimal distance thresholds using the proposed method from the uncoarse extraction plane points to each plane boundary point of cottage and pantry were 0.025 m and 0.030 m, respectively. The highest correct rate and the highest error rate of the cottage’s (pantry’s) plane segmentations using the proposed method under the optimal distance threshold were 99.93% and 2.30% (98.55% and 2.44%), respectively. The F1 score value of the cottage’s and pantry’s plane segmentations using the proposed method under the optimal distance threshold reached 97.56% and 95.75%, respectively. This method can segment different objects on the same plane, while the random sample consensus (RANSAC) algorithm causes the plane to become over-segmented. The proposed method can also extract the coplanar points at the intersection of two planes, which cannot be separated using the region growing algorithm. Although the RANSAC-RG method combining the RANSAC algorithm and the region growing algorithm can optimize the segmentation results of the RANSAC (region growing) algorithm and has little difference in segmentation effect (especially for cottage data) with the proposed method, the method still loses coplanar points at some intersection of the two planes.https://www.mdpi.com/2072-4292/14/1/95building planepoint cloudsregion growing algorithmdistance algorithmboundary points |
| spellingShingle | Zhonghua Su Zhenji Gao Guiyun Zhou Shihua Li Lihui Song Xukun Lu Ning Kang Building Plane Segmentation Based on Point Clouds Remote Sensing building plane point clouds region growing algorithm distance algorithm boundary points |
| title | Building Plane Segmentation Based on Point Clouds |
| title_full | Building Plane Segmentation Based on Point Clouds |
| title_fullStr | Building Plane Segmentation Based on Point Clouds |
| title_full_unstemmed | Building Plane Segmentation Based on Point Clouds |
| title_short | Building Plane Segmentation Based on Point Clouds |
| title_sort | building plane segmentation based on point clouds |
| topic | building plane point clouds region growing algorithm distance algorithm boundary points |
| url | https://www.mdpi.com/2072-4292/14/1/95 |
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