scholarly journals A microhardness indentation point cloud segmentation method based on voxel cloud connectivity segmentation

2021 ◽  
Vol 18 ◽  
pp. 100124
Author(s):  
Shi Wei ◽  
Shi Chengyao ◽  
Chen Shilin ◽  
Zhang Kailin
Keyword(s):  
Point Cloud ◽  
Segmentation Method ◽  
Point Cloud Segmentation ◽  
Microhardness Indentation

Precise point cloud segmentation method based on distance judgment function

2021 ◽  
Author(s):  
YeYan Ning ◽  
ChunLei Wang ◽  
ZhenYu Zhang ◽  
YunJi Li
Keyword(s):  
Point Cloud ◽  
Distance Judgment ◽  
Segmentation Method ◽  
Point Cloud Segmentation

scholarly journals Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering

2020 ◽  
Vol 37 (6) ◽  
pp. 1019-1027
Author(s):  
Ali Saglam ◽  
Hasan B. Makineci ◽  
Ömer K. Baykan ◽  
Nurdan Akhan Baykan
Keyword(s):  
Point Cloud ◽  
Point Group ◽  
Three Dimensional ◽  
Point Clouds ◽  
Segmentation Method ◽  
Train Station ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Point Cloud Segmentation ◽  
Geometric Cues

Point cloud processing is a struggled field because the points in the clouds are three-dimensional and irregular distributed signals. For this reason, the points in the point clouds are mostly sampled into regularly distributed voxels in the literature. Voxelization as a pretreatment significantly accelerates the process of segmenting surfaces. The geometric cues such as plane directions (normals) in the voxels are mostly used to segment the local surfaces. However, the sampling process may include a non-planar point group (patch), which is mostly on the edges and corners, in a voxel. These voxels can cause misleading the segmentation process. In this paper, we separate the non-planar patches into planar sub-patches using k-means clustering. The largest one among the planar sub-patches replaces the normal and barycenter properties of the voxel with those of itself. We have tested this process in a successful point cloud segmentation method and measure the effects of the proposed method on two point cloud segmentation datasets (Mosque and Train Station). The method increases the accuracy success of the Mosque dataset from 83.84% to 87.86% and that of the Train Station dataset from 85.36% to 87.07%.

scholarly journals MLS POINT CLOUD SEGMENTATION BASED ON FEATURE POINTS OF SCANLINES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1007-1013
Author(s):  
R. Honma ◽  
H. Date ◽  
S. Kanai
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Spatial Distance ◽  
Feature Points ◽  
Segmentation Method ◽  
Point Reduction ◽  
Point Cloud Segmentation ◽  
Edge Points ◽  
The Difference

<p><strong>Abstract.</strong> Point clouds acquired using Mobile Laser Scanning (MLS) are applied to extract road information such as curb stones, road markings, and road side objects. In this paper, we present a scanline-based MLS point cloud segmentation method for various road and road side objects. First, end points of the scanline, jump edge points, and corner points are extracted as feature points. The feature points are then interpolated to accurately extract irregular parts consisting of irregularly distributed points such as vegetation. Next, using a point reduction method, additional feature points on a smooth surface are extracted for segmentation at the edges of the curb cut. Finally, points between the feature points are extracted as flat segments on the scanline, and continuing feature points are extracted as irregular segments on the scanline. Furthermore, these segments on the scanline are integrated as flat or irregular regions. In the extraction of the feature points, neighboring points based on the spatial distance are used to avoid being influenced by the difference in the point density. Based on experiments, the effectiveness of the proposed method was indicated based on an application to an MLS point cloud.</p>

scholarly journals Part point cloud segmentation method based on model registration

2021 ◽  
Vol 1884 (1) ◽  
pp. 012022
Author(s):  
Lei Si ◽  
Han Yang ◽  
Zhongming Li ◽  
Liming Duan
Keyword(s):  
Point Cloud ◽  
Segmentation Method ◽  
Point Cloud Segmentation

Point Cloud Segmentation Method for Complex Micro-Surface Based on Feature Line Fitting

2020 ◽  
Vol 57 (6) ◽  
pp. 061502
Author(s):  
张溪溪 Zhang Xixi ◽  
纪小刚 Ji Xiaogang ◽  
胡海涛 Hu Haitao ◽  
栾宇豪 Luan Yuhao ◽  
张建安 Zhang Jian''an
Keyword(s):  
Point Cloud ◽  
Segmentation Method ◽  
Point Cloud Segmentation ◽  
Feature Line ◽  
Line Fitting

scholarly journals POINT CLOUD SEGMENTATION OF GULLY BASED ON CHARACTERISTIC DIFFERENCE USING AIRBORNE LIDAR DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 307-311
Author(s):  
Y. Xu ◽  
D. Yue ◽  
P. He
Keyword(s):  
Clustering Analysis ◽  
Point Cloud ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Segmentation Method ◽  
Characteristic Difference ◽  
Point Cloud Segmentation ◽  
Cloud Characteristics ◽  
Airborne Lidar Data

The point cloud segmentation of gullies with high accuracy lays the groundwork for the gully parameter extraction and developing models. A point cloud segmentation method of gullies based on characteristic difference from airborne LIDAR is proposed. Firstly, point cloud characteristics of gullies are discussed, and then differences in surface features are obtained based on different scales after preprocessing of the point cloud. Initial gullies are segmented from point clouds combined with a curvature threshold. Finally, real gully point clouds are obtained based on the clustering analysis. The experimental results demonstrate that gullies can be detected accurately with airborne LIDAR point clouds, and this method provides a new idea for quantitative evaluation of gullies.

scholarly journals Exploiting a Semi-Automatic Point Cloud Segmentation Method to Improve the Quality of Rock-Mass Characterization. The Cima Grappa Conservative Restoration Case Study

2021 ◽  
Vol 10 (5) ◽  
pp. 276
Author(s):  
Francesco Mugnai ◽  
Paolo Farina ◽  
Grazia Tucci
Keyword(s):  
Rock Mass ◽  
Point Cloud ◽  
Laser Scanning ◽  
Automatic Segmentation ◽  
Point Clouds ◽  
Segmentation Method ◽  
Rock Mass Characterization ◽  
Point Cloud Segmentation ◽  
Conservative Restoration ◽  
The Military

This paper presents results from applying semi-automatic point cloud segmentation methods in the underground tunnels within the Military Shrine’s conservative restoration project in Cima Grappa (Italy). The studied area, which has a predominant underground development distributed in a network of tunnels, is characterized by diffuse rock collapsing. In such a context, carrying out surveys and other technical operations are dangerous activities. Considering safety restrictions and unreachable impervious tunnels, having approached the study area with the scan-line survey technique resulted in only partial rock mass characterization. Hence, the geo-mechanical dataset was integrated, applying a semi-automatic segmentation method to the point clouds acquired through terrestrial laser scanning (TLS). The combined approach allowed for remote performance of detailed rock mass characterization, even remotely, in a short time and with a limited operators presence on site. Moreover, it permitted extending assessing tunnels’ stability and state of conservation to the inaccessible areas.

Sign in / Sign up

Export Citation Format

Share Document