High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel
In respect of rock-mass engineering, the detection of planar structures from the rock-mass point clouds plays a crucial role in the construction of a lightweight numerical model, while the establishment of high-quality models relies on the accurate results of surface analysis. However, the existing...
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doaj-e085d5be03a345ca9ecbbef34a90d9aa2020-11-25T03:15:27ZengMDPI AGSensors1424-82202020-07-01204209420910.3390/s20154209High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on SupervoxelDongbo Yu0Jun Xiao1Ying Wang2School of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, ChinaSchool of Artificial Intelligence, University of Chinese Academy of Sciences, No. 19 Yuquan Road, Shijingshan District, Beijing 100049, ChinaIn respect of rock-mass engineering, the detection of planar structures from the rock-mass point clouds plays a crucial role in the construction of a lightweight numerical model, while the establishment of high-quality models relies on the accurate results of surface analysis. However, the existing techniques are barely capable to segment the rock mass thoroughly, which is attributed to the cluttered and unpredictable surface structures of the rock mass. This paper proposes a high-precision plane detection approach for 3D rock-mass point clouds, which is effective in dealing with the complex surface structures, thus achieving a high level of detail in detection. Firstly, the input point cloud is fast segmented to voxels using spatial grids, while the local coplanarity test and the edge information calculation are performed to extract the major segments of planes. Secondly, to preserve as much detail as possible, supervoxel segmentation instead of traditional region growing is conducted to deal with scattered points. Finally, a patch-based region growing strategy applicable to rock mass is developed, while the completed planes are obtained by merging supervoxel patches. In this paper, an artificial icosahedron point cloud and four rock-mass point clouds are applied to validate the performance of the proposed method. As indicated by the experimental results, the proposed method can make high-precision plane detection achievable for rock-mass point clouds while ensuring high recall rate. Furthermore, the results of both qualitative and quantitative analyses evidence the superior performance of our algorithm.https://www.mdpi.com/1424-8220/20/15/4209plane detectionhigh-precisionrock massvoxelsupervoxelpatch-based |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dongbo Yu Jun Xiao Ying Wang |
spellingShingle |
Dongbo Yu Jun Xiao Ying Wang High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel Sensors plane detection high-precision rock mass voxel supervoxel patch-based |
author_facet |
Dongbo Yu Jun Xiao Ying Wang |
author_sort |
Dongbo Yu |
title |
High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel |
title_short |
High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel |
title_full |
High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel |
title_fullStr |
High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel |
title_full_unstemmed |
High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel |
title_sort |
high-precision plane detection method for rock-mass point clouds based on supervoxel |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-07-01 |
description |
In respect of rock-mass engineering, the detection of planar structures from the rock-mass point clouds plays a crucial role in the construction of a lightweight numerical model, while the establishment of high-quality models relies on the accurate results of surface analysis. However, the existing techniques are barely capable to segment the rock mass thoroughly, which is attributed to the cluttered and unpredictable surface structures of the rock mass. This paper proposes a high-precision plane detection approach for 3D rock-mass point clouds, which is effective in dealing with the complex surface structures, thus achieving a high level of detail in detection. Firstly, the input point cloud is fast segmented to voxels using spatial grids, while the local coplanarity test and the edge information calculation are performed to extract the major segments of planes. Secondly, to preserve as much detail as possible, supervoxel segmentation instead of traditional region growing is conducted to deal with scattered points. Finally, a patch-based region growing strategy applicable to rock mass is developed, while the completed planes are obtained by merging supervoxel patches. In this paper, an artificial icosahedron point cloud and four rock-mass point clouds are applied to validate the performance of the proposed method. As indicated by the experimental results, the proposed method can make high-precision plane detection achievable for rock-mass point clouds while ensuring high recall rate. Furthermore, the results of both qualitative and quantitative analyses evidence the superior performance of our algorithm. |
topic |
plane detection high-precision rock mass voxel supervoxel patch-based |
url |
https://www.mdpi.com/1424-8220/20/15/4209 |
work_keys_str_mv |
AT dongboyu highprecisionplanedetectionmethodforrockmasspointcloudsbasedonsupervoxel AT junxiao highprecisionplanedetectionmethodforrockmasspointcloudsbasedonsupervoxel AT yingwang highprecisionplanedetectionmethodforrockmasspointcloudsbasedonsupervoxel |
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