Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

Liu, Lupeng; Xiao, Jun; Wang, Ying

doi:10.3390/rs11060635

Cited by 18 publications

(21 citation statements)

References 49 publications

(80 reference statements)

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“…RG [ 33 ], RHT [ 53 ] and RAN-PCL [ 54 ], respectively represent three traditional plane detection methods. HT-RG [ 44 ], DSE [ 43 ], MOE [ 45 ], RAN-RG [ 46 ] are four plane detection methods for rock mass. The basic principle of each method can be found in Section 2.3 .…”

Section: Resultsmentioning

confidence: 99%

“…Leng et al [ 44 ] proposed a multi-scale rock surface detection method based on HT and RG, with multi-scale plane detection performed by adjusting parameters manually. Liu et al [ 45 ] put forward an efficient detection strategy, where coplanarity test was conducted to simplify point clouds in an efficient way. Then, HT was used exclusively to calculate the major orientation in coplanar patches, while the surfaces of rock mass were extracted by hybrid region growing (HRG).…”

Section: Related Workmentioning

confidence: 99%

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High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel

Yu¹,

Xiao²,

Wang³

2020

Sensors

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel

Yu¹,

Xiao²,

Wang³

2020

Sensors

Self Cite

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“…These technologies can directly collect 3D point clouds on the surface of rock masses in real time and with high precision, without being affected by subjective factors like manual contact measurement, thus making up for the shortcomings of traditional surveying and mapping. There are some successful cases of discontinuity orientations being extracted, as can be seen in the data obtained from LiDAR [10][11][12][13][14][15][16] and photogrammetry [17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces

Wang

et al. 2021

Remote Sensing

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Light detection and ranging (LiDAR) can quickly and accurately obtain 3D point clouds on the surface of rock masses, and on the basis of this, discontinuity information can be extracted automatically. This paper proposes a new method to automatically extract discontinuity information from 3D point clouds on the surface of rock masses. This method first applies the improved K-means algorithm based on the clustering algorithm by fast search and find of density peaks (DPCA) and the silhouette coefficient in the cluster validity index to identify the discontinuity sets of rock masses, and then uses the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to segment the discontinuity sets and to extract each discontinuity from a discontinuity set. Finally, the random sampling consistency (RANSAC) method is used to fit the discontinuities and to calculate their parameters. The 3D point clouds of the typical rock slope in the Rockbench repository is used to extract the discontinuity orientations using the new method, and these are compared with the results obtained from the classical approach and the previous automatic methods. The results show that, compared to the results obtained by Riquelme et al. in 2014, the average deviation of the dip direction and dip angle is reduced by 26% and 8%, respectively; compared to the results obtained by Chen et al. in 2016, the average deviation of the dip direction and dip angle is reduced by 39% and 40%, respectively. The method is also applied to an artificial quarry slope, and the average deviation of the dip direction and dip angle is 5.3° and 4.8°, respectively, as compared to the manual method. Furthermore, the related parameters are analyzed. The study shows that the new method is reliable, has a higher precision when identifying rock mass discontinuities, and can be applied to practical engineering.

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“…However, the discontinuity sets obtained directly through the raw point cloud are sensitive to the outliers and rely on the accuracy of local features [35], [36]. In the work of Liu et al (2019), clustering and major orientation estimating algorithms based on voxels and Gaussian Kernel is proposed to extract rock surfaces from the unorganized point cloud [37]. But the boundaries of the rock mass cannot be preserves well due to the fixed resolution is adapted in clustering processing.…”

Section: Introductionmentioning

confidence: 99%

Rock Mass Discontinuity Extraction Method Based on Multiresolution Supervoxel Segmentation of Point Cloud

Sun

Wang

Yang

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Discontinuity sets play an essential and pivotal role in the deformation monitoring and stability analysis of the rock mass, but there are still many challenges for accurately and rapidly extracting discontinuity. In this study, an extraction and characterization method of discontinuity sets based on point cloud supervoxel segmentation was proposed, which consists of four parts: (1) a multiresolution supervoxel segmentation (MRSS) algorithm was developed to classify unstructured point cloud into multiresolution facets and discrete points; (2) to extract the individual discontinuity, the single supervoxel that having spatial connectivity, similar planarity and parallelism was clustered; (3) the orientation of individual discontinuity were calculated respectively based on the plane fitting parameters; (4) for comprehensively analyzing the stability of rock mass, the improving k-means clustering algorithm is utilized to constructing the discontinuity sets that having similar orientation information. The novel method has been successfully tested on two practical cases (a rock cut and a side slope point cloud captured by the static terrestrial laser scanner). A comparison with existing methods shows that the deviation of the discontinuity orientation for rock cut is less than 1°, and the time efficiency is increased by 2.6 times. In addition, the orientation variation of the seven principle discontinuity in the five temporal side slope point cloud is relatively small, the dip direction and angle are within 2° and 1°, respectively. We can conclude that the proposed method can efficiently obtain the full extent of every individual discontinuity from rock mass surface point cloud and accurately analyze their orientation information.

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Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

Cited by 18 publications

References 49 publications

High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel

High-Precision Plane Detection Method for Rock-Mass Point Clouds Based on Supervoxel

A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces

Rock Mass Discontinuity Extraction Method Based on Multiresolution Supervoxel Segmentation of Point Cloud

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