Efficient Lightweight Surface Reconstruction Method from Rock-Mass Point Clouds
As for rock numerical calculation and stability analysis, it is essential to build a numerical model of rock mass with concise and accurate structure information through the three-dimensional surface reconstruction of rock-mass point clouds. However, the current research on lightweight surface recon...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/5/1200 |
| _version_ | 1827650717744103424 |
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| author | Dongbo Yu Jun Xiao Ying Wang |
| author_facet | Dongbo Yu Jun Xiao Ying Wang |
| author_sort | Dongbo Yu |
| collection | DOAJ |
| description | As for rock numerical calculation and stability analysis, it is essential to build a numerical model of rock mass with concise and accurate structure information through the three-dimensional surface reconstruction of rock-mass point clouds. However, the current research on lightweight surface reconstruction of non-artificial objects is very limited. In this paper, an efficient lightweight surface reconstruction method for rock-mass point clouds is proposed. Firstly, the input point cloud is segmented to obtain the plane primitives. In this process, the recognition of texture information and the complete over-segmentation of effective information play a vital role in the high-precision segmentation of rock surfaces. Secondly, the boundaries of all planes are reorganized according to the obvious connectivity in the segmentation results, so as to realize the assembly of the model, while solving all collision problems. Finally, an integer programming model is constructed to screen the best closure scheme of each plane, thus ensuring the best outcome of the reconstruction. In this study, seven groups of natural rock-mass point clouds are used to validate the proposed method. As suggested by the experimental results, this algorithm is effective in compressing the point cloud data of rock mass, to generate a watertight numerical model that can be directly used for simulation calculation. In addition, this method has strong robustness to noise and can effectively deal with highly corrupted rock-mass point cloud data. |
| first_indexed | 2024-03-09T20:22:57Z |
| format | Article |
| id | doaj.art-0f37c2f4dcd749a5a44fb1af52503ef9 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T20:22:57Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0f37c2f4dcd749a5a44fb1af52503ef92023-11-23T23:43:00ZengMDPI AGRemote Sensing2072-42922022-02-01145120010.3390/rs14051200Efficient Lightweight Surface Reconstruction Method from Rock-Mass Point CloudsDongbo Yu0Jun Xiao1Ying Wang2School of Artificial Intelligence, University of Chinese Academy and Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, ChinaSchool of Artificial Intelligence, University of Chinese Academy and Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, ChinaSchool of Artificial Intelligence, University of Chinese Academy and Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, ChinaAs for rock numerical calculation and stability analysis, it is essential to build a numerical model of rock mass with concise and accurate structure information through the three-dimensional surface reconstruction of rock-mass point clouds. However, the current research on lightweight surface reconstruction of non-artificial objects is very limited. In this paper, an efficient lightweight surface reconstruction method for rock-mass point clouds is proposed. Firstly, the input point cloud is segmented to obtain the plane primitives. In this process, the recognition of texture information and the complete over-segmentation of effective information play a vital role in the high-precision segmentation of rock surfaces. Secondly, the boundaries of all planes are reorganized according to the obvious connectivity in the segmentation results, so as to realize the assembly of the model, while solving all collision problems. Finally, an integer programming model is constructed to screen the best closure scheme of each plane, thus ensuring the best outcome of the reconstruction. In this study, seven groups of natural rock-mass point clouds are used to validate the proposed method. As suggested by the experimental results, this algorithm is effective in compressing the point cloud data of rock mass, to generate a watertight numerical model that can be directly used for simulation calculation. In addition, this method has strong robustness to noise and can effectively deal with highly corrupted rock-mass point cloud data.https://www.mdpi.com/2072-4292/14/5/1200lightweightsurface reconstructionpoint cloudrock mass |
| spellingShingle | Dongbo Yu Jun Xiao Ying Wang Efficient Lightweight Surface Reconstruction Method from Rock-Mass Point Clouds Remote Sensing lightweight surface reconstruction point cloud rock mass |
| title | Efficient Lightweight Surface Reconstruction Method from Rock-Mass Point Clouds |
| title_full | Efficient Lightweight Surface Reconstruction Method from Rock-Mass Point Clouds |
| title_fullStr | Efficient Lightweight Surface Reconstruction Method from Rock-Mass Point Clouds |
| title_full_unstemmed | Efficient Lightweight Surface Reconstruction Method from Rock-Mass Point Clouds |
| title_short | Efficient Lightweight Surface Reconstruction Method from Rock-Mass Point Clouds |
| title_sort | efficient lightweight surface reconstruction method from rock mass point clouds |
| topic | lightweight surface reconstruction point cloud rock mass |
| url | https://www.mdpi.com/2072-4292/14/5/1200 |
| work_keys_str_mv | AT dongboyu efficientlightweightsurfacereconstructionmethodfromrockmasspointclouds AT junxiao efficientlightweightsurfacereconstructionmethodfromrockmasspointclouds AT yingwang efficientlightweightsurfacereconstructionmethodfromrockmasspointclouds |