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.

scholarly journals An Efficient Encoding Voxel-Based Segmentation (EVBS) Algorithm Based on Fast Adjacent Voxel Search for Point Cloud Plane Segmentation

2019 ◽  
Vol 11 (23) ◽  
pp. 2727 ◽  
Author(s):  
Ming Huang ◽  
Pengcheng Wei ◽  
Xianglei Liu
Keyword(s):  
Point Cloud ◽  
Initial Point ◽  
Computation Time ◽  
Region Growing ◽  
Point Clouds ◽  
Recall Rate ◽  
Seed Selection ◽  
Cloud Processing ◽  
Point Cloud Segmentation ◽  
Noise Data

Plane segmentation is a basic yet important process in light detection and ranging (LiDAR) point cloud processing. The traditional point cloud plane segmentation algorithm is typically affected by the number of point clouds and the noise data, which results in slow segmentation efficiency and poor segmentation effect. Hence, an efficient encoding voxel-based segmentation (EVBS) algorithm based on a fast adjacent voxel search is proposed in this study. First, a binary octree algorithm is proposed to construct the voxel as the segmentation object and code the voxel, which can compute voxel features quickly and accurately. Second, a voxel-based region growing algorithm is proposed to cluster the corresponding voxel to perform the initial point cloud segmentation, which can improve the rationality of seed selection. Finally, a refining point method is proposed to solve the problem of under-segmentation in unlabeled voxels by judging the relationship between the points and the segmented plane. Experimental results demonstrate that the proposed algorithm is better than the traditional algorithm in terms of computation time, extraction accuracy, and recall rate.

Rock mass deterioration model of bank slope based on high-precision 3D multiperiod point clouds in the Three Gorges Reservoir, China

2021 ◽  
pp. qjegh2020-100
Author(s):  
Guoqiang Yan ◽  
Bolin Huang ◽  
Zhen Qin ◽  
Zhenwei Dai ◽  
Quan Zhang
Keyword(s):  
Rock Mass ◽  
High Precision ◽  
Point Cloud ◽  
Three Gorges Reservoir ◽  
Point Clouds ◽  
Three Gorges ◽  
The Three Gorges Reservoir ◽  
Response Characteristics ◽  
The Three Gorges ◽  
Cloud Response

Karst bank slopes are widely developed in the Three Gorges Reservoir area. Potential bank slope instability caused by the deterioration of rock masses has aroused considerable attention. This paper describes an attempt to use high-precision 3D laser scanning point cloud technology to reveal the deterioration law of a rock mass from a meso-/macroscale perspective and to preliminarily establish the relationship between the characteristics of the rock mass deterioration and 3D point cloud response. The main conclusions are as follows. The point cloud response characteristics of rock mass deterioration can be divided into three types: crack manifestation–extension, collapse-block falling, and translation-rotation. The crack manifestation–extension type can be further subdivided into three subcategories: crack extension, crack widening and crack deepening. These three subclasses of point cloud response characteristics of crack manifestation–extension all exhibit strip-like erosion-induced extensions, but the deformation forms and locations are slightly different. The point cloud response of collapse-block falling shows irregular sheet erosion. The point cloud response characteristics of translational rotation are similar, but associated erosion deformation often presents regular isosurface deformation characteristics. The C2C and M3C2 algorithms are used to calculate the deterioration and deformation, and the difference between recording points at the same location is small, except where the local normal vector N→ changes greatly or the point cloud is sparse, where there are large differences. Based on 3D point clouds, the deterioration rate formula De = n/N for a rock mass is proposed for the first time, where n is the number of degraded and deformed point clouds, and N is the total number of point clouds in the test area. According to the deterioration rates De corresponding to different deformation thresholds, the deterioration degree and state of a rock mass can be described and judged.

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.

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 SEGMENTATION OF LARGE UNSTRUCTURED POINT CLOUDS USING OCTREE-BASED REGION GROWING AND CONDITIONAL RANDOM FIELDS

2017 ◽  
Vol XLII-2/W8 ◽  
pp. 25-30 ◽  
Author(s):  
M. Bassier ◽  
M. Bonduel ◽  
B. Van Genechten ◽  
M. Vergauwen
Keyword(s):  
Random Fields ◽  
Point Cloud ◽  
Conditional Random Fields ◽  
Conditional Random Field ◽  
Region Growing ◽  
Point Clouds ◽  
Single Type ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Point Cloud Segmentation

Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it is a key aspect in the automated procedure from point cloud data to BIM. Current approaches typically only segment a single type of primitive such as planes or cylinders. Also, current algorithms suffer from oversegmenting the data and are often sensor or scene dependent.<br><br> In this work, a method is presented to automatically segment large unstructured point clouds of buildings. More specifically, the segmentation is formulated as a graph optimisation problem. First, the data is oversegmented with a greedy octree-based region growing method. The growing is conditioned on the segmentation of planes as well as smooth surfaces. Next, the candidate clusters are represented by a Conditional Random Field after which the most likely configuration of candidate clusters is computed given a set of local and contextual features. The experiments prove that the used method is a fast and reliable framework for unstructured point cloud segmentation. Processing speeds up to 40,000 points per second are recorded for the region growing. Additionally, the recall and precision of the graph clustering is approximately 80%. Overall, nearly 22% of oversegmentation is reduced by clustering the data. These clusters will be classified and used as a basis for the reconstruction of BIM models.

scholarly journals Leaf Segmentation on Dense Plant Point Clouds with Facet Region Growing

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3625 ◽  
Author(s):  
Dawei Li ◽  
Yan Cao ◽  
Xue-song Tang ◽  
Siyuan Yan ◽  
Xin Cai
Keyword(s):  
Point Cloud ◽  
Active Material ◽  
Region Growing ◽  
Point Clouds ◽  
Phenotypic Traits ◽  
Individual Leaf ◽  
3D Point Clouds ◽  
Segmentation Approach ◽  
Monstera Deliciosa ◽  
Leaf Segmentation

Leaves account for the largest proportion of all organ areas for most kinds of plants, and are comprise the main part of the photosynthetically active material in a plant. Observation of individual leaves can help to recognize their growth status and measure complex phenotypic traits. Current image-based leaf segmentation methods have problems due to highly restricted species and vulnerability toward canopy occlusion. In this work, we propose an individual leaf segmentation approach for dense plant point clouds using facet over-segmentation and facet region growing. The approach can be divided into three steps: (1) point cloud pre-processing, (2) facet over-segmentation, and (3) facet region growing for individual leaf segmentation. The experimental results show that the proposed method is effective and efficient in segmenting individual leaves from 3D point clouds of greenhouse ornamentals such as Epipremnum aureum, Monstera deliciosa, and Calathea makoyana, and the average precision and recall are both above 90%. The results also reveal the wide applicability of the proposed methodology for point clouds scanned from different kinds of 3D imaging systems, such as stereo vision and Kinect v2. Moreover, our method is potentially applicable in a broad range of applications that aim at segmenting regular surfaces and objects from a point cloud.

scholarly journals Automated Feature-Based Down-Sampling Approaches for Fine Registration of Irregular Point Clouds

2020 ◽  
Vol 12 (7) ◽  
pp. 1224 ◽  
Author(s):  
Abdulla Al-Rawabdeh ◽  
Fangning He ◽  
Ayman Habib
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Geometric Constraints ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Transformation Parameters ◽  
Matching Techniques ◽  
Mean Square Errors ◽  
Fine Registration ◽  
Sampling Approach

The integration of three-dimensional (3D) data defined in different coordinate systems requires the use of well-known registration procedures, which aim to align multiple models relative to a common reference frame. Depending on the achieved accuracy of the estimated transformation parameters, the existing registration procedures are classified as either coarse or fine registration. Coarse registration is typically used to establish a rough alignment between the involved point clouds. Fine registration starts from coarsely aligned point clouds to achieve more precise alignment of the involved datasets. In practice, the acquired/derived point clouds from laser scanning and image-based dense matching techniques usually include an excessive number of points. Fine registration of huge datasets is time-consuming and sometimes difficult to accomplish in a reasonable timeframe. To address this challenge, this paper introduces two down-sampling approaches, which aim to improve the efficiency and accuracy of the iterative closest patch (ICPatch)-based fine registration. The first approach is based on a planar-based adaptive down-sampling strategy to remove redundant points in areas with high point density while keeping the points in lower density regions. The second approach starts with the derivation of the surface normals for the constituents of a given point cloud using their local neighborhoods, which are then represented on a Gaussian sphere. Down-sampling is ultimately achieved by removing the points from the detected peaks in the Gaussian sphere. Experiments were conducted using both simulated and real datasets to verify the feasibility of the proposed down-sampling approaches for providing reliable transformation parameters. Derived experimental results have demonstrated that for most of the registration cases, in which the points are obtained from various mapping platforms (e.g., mobile/static laser scanner or aerial photogrammetry), the first proposed down-sampling approach (i.e., adaptive down-sampling approach) was capable of exceeding the performance of the traditional approaches, which utilize either the original or randomly down-sampled points, in terms of providing smaller Root Mean Square Errors (RMSE) values and a faster convergence rate. However, for some challenging cases, in which the acquired point cloud only has limited geometric constraints, the Gaussian sphere-based approach was capable of providing superior performance as it preserves some critical points for the accurate estimation of the transformation parameters relating the involved point clouds.

scholarly journals AUTOMATIC EXTRACTION AND RECOGNITION OF ROAD MARKINGS BASED ON VEHICLE LASER POINT CLOUD

2020 ◽  
Vol V-2-2020 ◽  
pp. 313-319
Author(s):  
L. Yao ◽  
C. Qin ◽  
Q. Chen ◽  
H. Wu ◽  
S. Zhang
Keyword(s):  
High Precision ◽  
Point Cloud ◽  
Vertical Section ◽  
Point Clouds ◽  
Binary Image ◽  
Road Surface ◽  
Surface Point ◽  
Buffer Area ◽  
The Road ◽  
Automatic Driving

Abstract. At present, automatic driving technology has become one of the development direction of the future intelligent transportation system. The high high-precision map, which is an important supplement of the on on-board sensors under the condition of shielding or the restriction of observation distance, provides a priori information for high high-precision positioning and path planning of the automatic driving with the level of L3 and above. The position and semantic information of the road markings, such as the absolute coordinates of th e solid line and the bro ken line, are the basic components of the high high-precision map. At present, point cloud data are still one of the most important data source of the high high-precision map. So, how to get road markings information from original point clouds automatically deserve study. In this paper, point cloud is sliced by the mileage of the road, then each slice is projected onto respective vertical section section. Random Sample Consensus (RANSAC) algorithm is applied to establish road surface buffer area . Finally, moving window filtering is used to extract road surface point cloud from road surface buffer area area. On this basis, the road surface point cloud image is transformed into raster image with a certain resolution by using the method of inverse distance weighted interpolation , and the grid image is converted into binary image by using the method of adaptive threshold segmentation based on the integral graph. Then the method of the Euclidean clustering is used to extract the road markings point cloud from the binary image. Characteristic attribute detection is applied to recognize solid line marking from all clusters. Deep learning network framework pointnet++ is applied to recognize remain road markings including guideline, broken line, straight arrow, and right turn arrow.

scholarly journals Building Plane Segmentation Based on Point Clouds

2021 ◽  
Vol 14 (1) ◽  
pp. 95
Author(s):  
Zhonghua Su ◽  
Zhenji Gao ◽  
Guiyun Zhou ◽  
Shihua Li ◽  
Lihui Song ◽  
...  
Keyword(s):  
Point Cloud ◽  
Region Growing ◽  
Point Clouds ◽  
Plane Boundary ◽  
Point Cloud Data ◽  
Distance Threshold ◽  
Cloud Data ◽  
Optimal Distance ◽  
Building Plane ◽  
Ransac Algorithm

Planes are essential features to describe the shapes of buildings. The segmentation of a plane is significant when reconstructing a building in three dimensions. However, there is a concern about the accuracy in segmenting plane from point cloud data. The objective of this paper was to develop an effective segmentation algorithm for building planes that combines the region growing algorithm with the distance algorithm based on boundary points. The method was tested on point cloud data from a cottage and pantry as scanned using a Faro Focus 3D laser range scanner and Matterport Camera, respectively. A coarse extraction of the building plane was obtained from the region growing algorithm. The coplanar points where two planes intersect were obtained from the distance algorithm. The building plane’s optimal segmentation was then obtained by combining the coarse extraction plane points and the corresponding coplanar points. The results show that the proposed method successfully segmented the plane points of the cottage and pantry. The optimal distance thresholds using the proposed method from the uncoarse extraction plane points to each plane boundary point of cottage and pantry were 0.025 m and 0.030 m, respectively. The highest correct rate and the highest error rate of the cottage’s (pantry’s) plane segmentations using the proposed method under the optimal distance threshold were 99.93% and 2.30% (98.55% and 2.44%), respectively. The F1 score value of the cottage’s and pantry’s plane segmentations using the proposed method under the optimal distance threshold reached 97.56% and 95.75%, respectively. This method can segment different objects on the same plane, while the random sample consensus (RANSAC) algorithm causes the plane to become over-segmented. The proposed method can also extract the coplanar points at the intersection of two planes, which cannot be separated using the region growing algorithm. Although the RANSAC-RG method combining the RANSAC algorithm and the region growing algorithm can optimize the segmentation results of the RANSAC (region growing) algorithm and has little difference in segmentation effect (especially for cottage data) with the proposed method, the method still loses coplanar points at some intersection of the two planes.

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