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

2019 ◽  
Vol 11 (6) ◽  
pp. 635 ◽  
Author(s):  
Lupeng Liu ◽  
Jun Xiao ◽  
Ying Wang
Keyword(s):  
Rock Mass ◽  
3D Modeling ◽  
Point Cloud ◽  
Rapid Development ◽  
Point Clouds ◽  
Engineering Calculation ◽  
Mass Point ◽  
Gaussian Kernel ◽  
Rock Surface ◽  
Surface Extraction

In the fields of 3D modeling, analysis of discontinuities and engineering calculation, surface extraction is of great importance. The rapid development of photogrammetry and Light Detection and Ranging (LiDAR) technology facilitates the study of surface extraction. Automatic extraction of rock surfaces from 3D rock-mass point clouds also becomes the basis of 3D modeling and engineering calculation of rock mass. This paper presents an automated and effective method for extracting rock surfaces from unorganized rock-mass point clouds. This method consists of three stages: (i) clustering based on voxels; (ii) estimating major orientations based on Gaussian Kernel and (iii) rock surface extraction. Firstly, the two-level spatial grid is used for fast voxelization and segmenting the point cloud into three types of voxels, including coplanar, non-coplanar and sparse voxels. Secondly, the coplanar voxels, rather than the scattered points, are employed to estimate major orientations by using a bivariate Gaussian Kernel. Finally, the seed voxels are selected on the basis of major orientations and the region growing method based on voxels is applied to extract rock surfaces, resulting in sets of surface clusters. The sub-surfaces of each cluster are coplanar or parallel. In this paper, artificial icosahedron point cloud and natural rock-mass point clouds are used for testing the proposed method, respectively. The experimental results show that, the proposed method can effectively and accurately extract rock surfaces in unorganized rock-mass point clouds.

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

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4209
Author(s):  
Dongbo Yu ◽  
Jun Xiao ◽  
Ying Wang
Keyword(s):  
Rock Mass ◽  
High Precision ◽  
Point Cloud ◽  
Region Growing ◽  
Complex Surface ◽  
Point Clouds ◽  
Recall Rate ◽  
Mass Point ◽  
Surface Structures ◽  
Superior Performance

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.

Towards automatic discontinuity trace extraction from rock mass point cloud without triangulation

2018 ◽  
Vol 112 ◽  
pp. 226-237 ◽  
Author(s):  
Jiateng Guo ◽  
Lixin Wu ◽  
Minmin Zhang ◽  
Shanjun Liu ◽  
Xiaoyu Sun
Keyword(s):  
Rock Mass ◽  
Point Cloud ◽  
Mass Point

scholarly journals An MFF-SLIC Hybrid Superpixel Segmentation Method with Multi-Source RS Data for Rock Surface Extraction

2019 ◽  
Vol 9 (5) ◽  
pp. 906 ◽  
Author(s):  
Xuefeng Yi ◽  
Rongchun Zhang ◽  
Hao Li ◽  
Yuanyuan Chen
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Distance Measure ◽  
Digital Images ◽  
Measurement Techniques ◽  
Rock Surface ◽  
Segmentation Method ◽  
Surface Extraction ◽  
Superpixel Segmentation ◽  
Relationship Construction

Multi-Source RS data integration is a crucial technology for rock surface extraction in geology. Both Terrestrial laser scanning (TLS) and Photogrammetry are primary non-contact active measurement techniques. In order to extract comprehensive and accurate rock surface information by the integration of TLS point cloud and digital images, the segmentation based on the integrated results generated by registration is the crux. This paper presents a Multi-Features Fusion for Simple Linear Iterative Clustering (MFF-SLIC) hybrid superpixel segmentation algorithm to extract the rock surface accurately. The MFF-SLIC algorithm mainly includes three contents: (1) Mapping relationship construction for TLS point cloud and digital images; (2) Distance measure model establishment with multi-features for initial superpixel segmentation; (3) Hierarchical and optimized clustering for superpixels. The proposed method was verified with the columnar basalt data, which is acquired in Guabushan Geopark in China. The results demonstrate that the segmentation method could be used for rock surface extraction with high precision and efficiency, the result of which would be prepared for further geological statistics and analysis.

scholarly journals A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification

2019 ◽  
Vol 9 (5) ◽  
pp. 951 ◽  
Author(s):  
Yong Li ◽  
Guofeng Tong ◽  
Xiance Du ◽  
Xiang Yang ◽  
Jianjun Zhang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Single Point ◽  
Point Clouds ◽  
Optimization Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Angle Distribution ◽  
Features Fusion ◽  
Cloud Classification ◽  
Point Cloud Classification

3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification algorithm based on a single point multilevel features fusion and pyramid neighborhood optimization are proposed for a Airborne Laser Scanning (ALS) point cloud. First, the proposed algorithm determines the neighborhood region of each point, after which the features of each single point are extracted. For the characteristics of the ALS point cloud, two new feature descriptors are proposed, i.e., a normal angle distribution histogram and latitude sampling histogram. Following this, multilevel features of a single point are constructed by multi-resolution of the point cloud and multi-neighborhood spaces. Next, the features are trained by the Support Vector Machine based on a Gaussian kernel function, and the points are classified by the trained model. Finally, a classification results optimization method based on a multi-scale pyramid neighborhood constructed by a multi-resolution point cloud is used. In the experiment, the algorithm is tested by a public dataset. The experimental results show that the proposed algorithm can effectively classify large-scale ALS point clouds. Compared with the existing algorithms, the proposed algorithm has a better classification performance.

scholarly journals EQUAL PROPORTION REPRODUCTION METHOD OF GROTTO BASED ON POINT CLOUD

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 1215-1219
Author(s):  
P. Wei ◽  
A. Li ◽  
M. Hou ◽  
L. Zhu ◽  
D. Xu ◽  
...  
Keyword(s):  
3D Printing ◽  
Point Cloud ◽  
Laser Scanning ◽  
New Technologies ◽  
Rapid Development ◽  
Point Clouds ◽  
Equal Proportion ◽  
3D Laser Scanning ◽  
Printing Technology ◽  
3D Printing Technology

<p><strong>Abstract.</strong> The rapid development of 3D laser scanning and 3D printing technology provides new technologies and ideas for cultural relic protection and reproduction. Aiming at the requirement of equal proportional reproduction of large-scale grottoes, this paper takes the point cloud data of the 18th Cave of Yungang Grottoes obtained by 3D laser scanning as an example, and proposes a data processing and reproduction block partitioning method for equal proportion reproduction. The Cyclone, Geomagic and AutoCAD software were used to construct the 3D model of the grotto, and the 3D printing technology was used to realize the secondary design and model print. Among them, the research focuses on the modeling of massive point clouds and the method of model partitioning based on voxels. It can meet the requirements of movable and assembly while realizing the equal proportional reproduction of the whole grotto. The research results and application can be a good reference for the future grotto reproduction work.</p>

Application of the 'Iterative Closest Point' Algorithm for Determination of Rotation Axis of a Turntable

2013 ◽  
Vol 199 ◽  
pp. 273-278
Author(s):  
Ireneusz Wróbel
Keyword(s):  
Reverse Engineering ◽  
Point Cloud ◽  
Rotation Axis ◽  
Rapid Development ◽  
Point Clouds ◽  
Iterative Closest Point ◽  
Mechanical Parts ◽  
Scanning Process ◽  
Iterative Closest Point Algorithm

Reverse engineering [ is a field of technology which has been under rapid development for several recent years. Optic scanners are basic devices used as reverse engineering tools. Point cloud describes the shape of a scanned object. Automatic turntable is a device which enables a scanning process from different viewing angles. In the paper, the algorithm is described which has been used for determination of rotation axis of a turntable. The obtained axis constitutes the base for an aggregation of particular point clouds into single resultant common cloud describing the shape of the scanned object. Usability of this algorithm for precise scanning of mechanical parts was validated, precision of shape replication was also evaluated.

scholarly journals ANALYSIS OF THE POSSIBILITIES OF USING LOW-COST SCANNING SYSTEM IN 3D MODELING

2016 ◽  
Vol XLI-B3 ◽  
pp. 261-267
Author(s):  
M. Kedzierski ◽  
D. Wierzbickia ◽  
A. Fryskowska ◽  
B. Chlebowska
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
Laser Scanning ◽  
Low Cost ◽  
Comparative Method ◽  
Research Work ◽  
Laser Scanner ◽  
Method Comparison ◽  
Point Clouds ◽  
Scanning System

The laser scanning technique is still a very popular and fast growing method of obtaining information on modeling 3D objects. The use of low-cost miniature scanners creates new opportunities for small objects of 3D modeling based on point clouds acquired from the scan. The same, the development of accuracy and methods of automatic processing of this data type is noticeable. The article presents methods of collecting raw datasets in the form of a point-cloud using a low-cost ground-based laser scanner FabScan. As part of the research work 3D scanner from an open source FabLab project was constructed. In addition, the results for the analysis of the geometry of the point clouds obtained by using a low-cost laser scanner were presented. Also, some analysis of collecting data of different structures (made of various materials such as: glass, wood, paper, gum, plastic, plaster, ceramics, stoneware clay etc. and of different shapes: oval and similar to oval and prism shaped) have been done. The article presents two methods used for analysis: the first one - visual (general comparison between the 3D model and the real object) and the second one - comparative method (comparison between measurements on models and scanned objects using the mean error of a single sample of observations). The analysis showed, that the low-budget ground-based laser scanner FabScan has difficulties with collecting data of non-oval objects. Items built of glass painted black also caused problems for the scanner. In addition, the more details scanned object contains, the lower the accuracy of the collected point-cloud is. Nevertheless, the accuracy of collected data (using oval-straight shaped objects) is satisfactory. The accuracy, in this case, fluctuates between ± 0,4 mm and ± 1,0 mm whereas when using more detailed objects or a rectangular shaped prism the accuracy is much more lower, between 2,9 mm and ± 9,0 mm. Finally, the publication presents the possibility (for the future expansion of research) of modernization FabScan by the implementation of a larger amount of camera-laser units. This will enable spots the registration , that are less visible.

scholarly journals Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5220
Author(s):  
Shima Sahebdivani ◽  
Hossein Arefi ◽  
Mehdi Maboudi
Keyword(s):  
3D Modeling ◽  
Point Cloud ◽  
3D Model ◽  
Three Dimensional ◽  
Point Clouds ◽  
Rail Track ◽  
Three Dimensional Modeling ◽  
Railway Industry ◽  
Dense Point ◽  
Uav Images

The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study.

scholarly journals 3D GEOLOGICAL OUTCROP CHARACTERIZATION: AUTOMATIC DETECTION OF 3D PLANES (AZIMUTH AND DIP) USING LiDAR POINT CLOUDS

2016 ◽  
Vol III-5 ◽  
pp. 105-112 ◽  
Author(s):  
K. Anders ◽  
M. Hämmerle ◽  
G. Miernik ◽  
T. Drews ◽  
A. Escalona ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Point Cloud ◽  
Laser Scanning ◽  
Spatial Information ◽  
Point Clouds ◽  
Rock Surface ◽  
Cloud Data ◽  
Surface Data ◽  
Data Volume

Terrestrial laser scanning constitutes a powerful method in spatial information data acquisition and allows for geological outcrops to be captured with high resolution and accuracy. A crucial aspect for numerous geologic applications is the extraction of rock surface orientations from the data. This paper focuses on the detection of planes in rock surface data by applying a segmentation algorithm directly to a 3D point cloud. Its performance is assessed considering (1) reduced spatial resolution of data and (2) smoothing in the course of data pre-processing. The methodology is tested on simulations of progressively reduced spatial resolution defined by varying point cloud density. Smoothing of the point cloud data is implemented by modifying the neighborhood criteria during normals estima-tion. The considerable alteration of resulting planes emphasizes the influence of smoothing on the plane detection prior to the actual segmentation. Therefore, the parameter needs to be set in accordance with individual purposes and respective scales of studies. Fur-thermore, it is concluded that the quality of segmentation results does not decline even when the data volume is significantly reduced down to 10%. The azimuth and dip values of individual segments are determined for planes fit to the points belonging to one segment. Based on these results, azimuth and dip as well as strike character of the surface planes in the outcrop are assessed. Thereby, this paper contributes to a fully automatic and straightforward workflow for a comprehensive geometric description of outcrops in 3D.

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