Robust Extraction of 3D Line Segment Features from Unorganized Building Point Clouds
As one of the most common features, 3D line segments provide visual information in scene surfaces and play an important role in many applications. However, due to the huge, unstructured, and non-uniform characteristics of building point clouds, 3D line segment extraction is a complicated task. This...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/14/3279 |
| _version_ | 1797416120862375936 |
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| author | Pengju Tian Xianghong Hua Wuyong Tao Miao Zhang |
| author_facet | Pengju Tian Xianghong Hua Wuyong Tao Miao Zhang |
| author_sort | Pengju Tian |
| collection | DOAJ |
| description | As one of the most common features, 3D line segments provide visual information in scene surfaces and play an important role in many applications. However, due to the huge, unstructured, and non-uniform characteristics of building point clouds, 3D line segment extraction is a complicated task. This paper presents a novel method for extraction of 3D line segment features from an unorganized building point cloud. Given the input point cloud, three steps were performed to extract 3D line segment features. Firstly, we performed data pre-processing, including subsampling, filtering and projection. Secondly, a projection-based method was proposed to divide the input point cloud into vertical and horizontal planes. Finally, for each 3D plane, all points belonging to it were projected onto the fitting plane, and the α-shape algorithm was exploited to extract the boundary points of each plane. The 3D line segment structures were extracted from the boundary points, followed by a 3D line segment merging procedure. Corresponding experiments demonstrate that the proposed method works well in both high-quality TLS and low-quality RGB-D point clouds. Moreover, the robustness in the presence of a high degree of noise is also demonstrated. A comparison with state-of-the-art techniques demonstrates that our method is considerably faster and scales significantly better than previous ones. To further verify the effectiveness of the line segments extracted by the proposed method, we also present a line-based registration framework, which employs the extracted 2D-projected line segments for coarse registration of building point clouds. |
| first_indexed | 2024-03-09T05:59:34Z |
| format | Article |
| id | doaj.art-26dfbbfe54214af489fa3caa368ee6f3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T05:59:34Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-26dfbbfe54214af489fa3caa368ee6f32023-12-03T12:10:19ZengMDPI AGRemote Sensing2072-42922022-07-011414327910.3390/rs14143279Robust Extraction of 3D Line Segment Features from Unorganized Building Point CloudsPengju Tian0Xianghong Hua1Wuyong Tao2Miao Zhang3Engineering Research Center of Environmental Laser Remote Sensing Technology and Application of Henan Province, Nanyang Normal University, Wolong Road No. 1638, Nanyang 473061, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaSchool of Mathematics and Computer Science, Nanchang University, Nanchang 330031, ChinaEngineering Research Center of Environmental Laser Remote Sensing Technology and Application of Henan Province, Nanyang Normal University, Wolong Road No. 1638, Nanyang 473061, ChinaAs one of the most common features, 3D line segments provide visual information in scene surfaces and play an important role in many applications. However, due to the huge, unstructured, and non-uniform characteristics of building point clouds, 3D line segment extraction is a complicated task. This paper presents a novel method for extraction of 3D line segment features from an unorganized building point cloud. Given the input point cloud, three steps were performed to extract 3D line segment features. Firstly, we performed data pre-processing, including subsampling, filtering and projection. Secondly, a projection-based method was proposed to divide the input point cloud into vertical and horizontal planes. Finally, for each 3D plane, all points belonging to it were projected onto the fitting plane, and the α-shape algorithm was exploited to extract the boundary points of each plane. The 3D line segment structures were extracted from the boundary points, followed by a 3D line segment merging procedure. Corresponding experiments demonstrate that the proposed method works well in both high-quality TLS and low-quality RGB-D point clouds. Moreover, the robustness in the presence of a high degree of noise is also demonstrated. A comparison with state-of-the-art techniques demonstrates that our method is considerably faster and scales significantly better than previous ones. To further verify the effectiveness of the line segments extracted by the proposed method, we also present a line-based registration framework, which employs the extracted 2D-projected line segments for coarse registration of building point clouds.https://www.mdpi.com/2072-4292/14/14/3279plane segmentationcontour point extraction3D line segmentsunorganized point clouds2D line featurepoint cloud registration |
| spellingShingle | Pengju Tian Xianghong Hua Wuyong Tao Miao Zhang Robust Extraction of 3D Line Segment Features from Unorganized Building Point Clouds Remote Sensing plane segmentation contour point extraction 3D line segments unorganized point clouds 2D line feature point cloud registration |
| title | Robust Extraction of 3D Line Segment Features from Unorganized Building Point Clouds |
| title_full | Robust Extraction of 3D Line Segment Features from Unorganized Building Point Clouds |
| title_fullStr | Robust Extraction of 3D Line Segment Features from Unorganized Building Point Clouds |
| title_full_unstemmed | Robust Extraction of 3D Line Segment Features from Unorganized Building Point Clouds |
| title_short | Robust Extraction of 3D Line Segment Features from Unorganized Building Point Clouds |
| title_sort | robust extraction of 3d line segment features from unorganized building point clouds |
| topic | plane segmentation contour point extraction 3D line segments unorganized point clouds 2D line feature point cloud registration |
| url | https://www.mdpi.com/2072-4292/14/14/3279 |
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