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.

scholarly journals LASER SCANNING AND POINT CLOUD SEGMENTATION FOR CONTACTLESS GEO-MECHANICAL SURVEYING: CONSERVATIVE RESTORATION IN HYPOGEUM ENVIRONMENT

2021 ◽  
Vol XLVI-M-1-2021 ◽  
pp. 455-461
Author(s):  
F. Mugnai
Keyword(s):  
Rock Mass ◽  
Point Cloud ◽  
Laser Scanning ◽  
Coating Materials ◽  
Point Cloud Segmentation ◽  
Survey Technique ◽  
Advanced Analysis ◽  
Scan Line ◽  
Conservative Restoration ◽  
The Military

Abstract. The work presents a survey campaign specifically designed to formulate an effective restoration project in a Cultural Heritage context, the Military Shrine in Cima Grappa (Italy). Several outputs have been generated by exploring the most advanced laser scanning survey technique and some specific point cloud analysis algorithms. A detailed geometrical 3D reconstruction of human-made and natural tunnels coating materials, a geo-mechanical survey of the rock mass, a map of rock collapses and cinematic analysis of instability processes.Integrating Laser Scanning technique with the Scan-line survey allowed to perform advanced analysis and rock-mass characterisation in a predominant subterranean developed area. Most of the tunnels and underground spaces displayed rock collapses and diffuse active instability processes that certainly could have drastically slowed down surveys and analysis. The adopted techniques allowed both to proceed in acquiring data end in delivering sound outputs rapidly.

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 INTEGRATING LASER SCANNING AND GEO-MECHANICAL SURVEY IN CONSERVATIVE RESTORATION; THE CASE OF FIRST WORLD WAR’S CIMA GRAPPA MILITARY SHRINE, ITALY.

2021 ◽  
Author(s):  
Francesco Mugnai ◽  
Paolo Farina ◽  
Grazia Tucci
Keyword(s):  
Rock Mass ◽  
Laser Scanning ◽  
General Description ◽  
Coating Materials ◽  
Restoration Project ◽  
Rock Mass Characterization ◽  
The One ◽  
Conservative Restoration ◽  
The Military ◽  
First World

The work presents results obtained performing a survey campaign specifically designed to formulate an effective restoration project in a critical context. Within the remarkable project, promoted and financed by the Italian Presidency of the Council of the Ministers, and the Italian Ministry of Defence, for designing the conservative restoration plan of the Military Shrine in Cima Grappa (Italy), the assessment of the overall tunnels’ stability and a report of the state of conservation of the underground area has been produced. Exploiting the most advanced laser scanning survey technique, and some specific algorithms for point cloud analysis, several outputs have been generated, in particular a detailed geometrical 3D reconstructions of man-made and natural tunnels coating materials, geo-mechanical survey of rock mass, map of rock collapses and cinematic analysis of instability processes. The integration of Laser Scanning technique with the most commonly used Scan-line survey for rock-mass characterization and architectural surveys, allowed to perform advances analysis even in a high-risk study area as the one considered in the restoration project, which is represented by a predominant subterranean development. Most of the tunnels and underground spaces, displayed rock collapses and diffuse active instability processes that certainly could have drastically slowed down surveys and analysis. The adopted techniques allowed to rapidly proceed in acquiring data end to deliver sound outputs. This paper aims to report both a general description of the project, spending some words on the historical value of the place and describing the complex environment of work, and a detailed depiction of the performed survey activities with particular attention in showing laser scanning survey and the obtained results.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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%.

Comparing traditional geomechanical and remote sensing techniques for rock mass characterization 

2021 ◽  
Author(s):  
Angela Caccia ◽  
Biagio Palma ◽  
Mario Parise
Keyword(s):  
Rock Mass ◽  
Point Cloud ◽  
Laser Scanning ◽  
Quantitative Description ◽  
Present Contribution ◽  
Rock Mass Characterization ◽  
Rock Face ◽  
Remote Sensing Techniques ◽  
The Stability ◽  
Processing Techniques

&lt;p&gt;Analysis of the stability conditions of rock masses starts from detailed geo-structural surveys based on a systematic and quantitative description of the systems of discontinuities. Traditionally, these surveys are performed by implementing the classical geomechanical systems, available in the scientific literature since several decades, through the use of simple tools such as the geological compass to measure dip and dip direction directly on the discontinuity systems, and to fully describe their more significant physical characteristics (length, spacing, roughness, persistence, aperture, filling, termination, etc.). In several cases, this can be difficult because the discontinuities, or even the rock face, cannot be easily accessible. To have a complete survey, very often the involvement of geologists climbers is required, but in many situations this work is not easy to carry out, and in any case it does not cover the whole rock front.&lt;/p&gt;&lt;p&gt;Today, to solve these problems, traditional geomechanical surveying is implemented by innovative remote techniques using, individually or in combination, instruments such as terrestrial laser scanners and unmanned aerial vehicles to build a point cloud.&lt;/p&gt;&lt;p&gt;This latter permits to extract very accurate data on discontinuities for stability analyses, based on areal and non-point observations. In addition, the point cloud allows to map sub-vertical walls in their entirety in much shorter times than traditional surveying.&lt;/p&gt;&lt;p&gt;At this regard, two rock slopes were detected in the Sorrento Peninsula (Campania, southern Italy) with techniques that include traditional mapping, dictated by the guidelines of the International Society for Rock Mechanics, and the remote survey, through laser scanning and drone photogrammetry. The data obtained were processed automatically and manually through the Dips, CloudCompare and Discontinuity Set Extractor softwares.&lt;/p&gt;&lt;p&gt;In the present contribution we highlight the limits and advantages of the main data collection and the processing techniques, and provide an evaluation of the software packages currently available for the analysis and evaluation of discontinuities, in order to obtain a better characterization of the rock mass.&lt;/p&gt;

scholarly journals SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 351-358 ◽  
Author(s):  
Hoang Long Nguyen ◽  
David Belton ◽  
Petra Helmholz
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Processing Steps ◽  
Carrier Vehicle

The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

scholarly journals SCAN PROFILES BASED METHOD FOR SEGMENTATION AND EXTRACTION OF PLANAR OBJECTS IN MOBILE LASER SCANNING POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 351-358 ◽  
Author(s):  
Hoang Long Nguyen ◽  
David Belton ◽  
Petra Helmholz
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Point Cloud Data ◽  
Segmentation Method ◽  
Cloud Data ◽  
Processing Steps ◽  
Carrier Vehicle

The demand for accurate spatial data has been increasing rapidly in recent years. Mobile laser scanning (MLS) systems have become a mainstream technology for measuring 3D spatial data. In a MLS point cloud, the point clouds densities of captured point clouds of interest features can vary: they can be sparse and heterogeneous or they can be dense. This is caused by several factors such as the speed of the carrier vehicle and the specifications of the laser scanner(s). The MLS point cloud data needs to be processed to get meaningful information e.g. segmentation can be used to find meaningful features (planes, corners etc.) that can be used as the inputs for many processing steps (e.g. registration, modelling) that are more difficult when just using the point cloud. Planar features are dominating in manmade environments and they are widely used in point clouds registration and calibration processes. There are several approaches for segmentation and extraction of planar objects available, however the proposed methods do not focus on properly segment MLS point clouds automatically considering the different point densities. This research presents the extension of the segmentation method based on planarity of the features. This proposed method was verified using both simulated and real MLS point cloud datasets. The results show that planar objects in MLS point clouds can be properly segmented and extracted by the proposed segmentation method.

scholarly journals Automated Reconstruction of Parametric BIM for Bridge Based on Terrestrial Laser Scanning Data

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Guocheng Qin ◽  
Yin Zhou ◽  
Kaixin Hu ◽  
Daguang Han ◽  
Chunli Ying
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Facility Management ◽  
Information Modeling ◽  
Geometric Features ◽  
Unit Structure ◽  
Point Cloud Segmentation ◽  
Reconstruction Quality ◽  
Building Information

Building information modeling (BIM) in industrialized bridge construction is usually performed based on initial design information. Differences exist between the model of the structure and its actual geometric dimensions and features due to the manufacturing, transportation, hoisting, assembly, and load bearing of the structure. These variations affect the construction project handover and facility management. The solutions available at present entail the use of point clouds to reconstruct BIM. However, these solutions still encounter problems, such as the inability to obtain the actual geometric features of a bridge quickly and accurately. Moreover, the created BIM is nonparametric and cannot be dynamically adjusted. This paper proposes a fully automatic method of reconstructing parameterized BIM by using point clouds to address the abovementioned problems. An algorithm for bridge point cloud segmentation is developed; the algorithm can separate the bridge point cloud from the entire scanning scene and segment the unit structure point cloud. Another algorithm for extracting the geometric features of the bridge point cloud is also proposed; this algorithm is effective for partially missing point clouds. The feasibility of the proposed method is evaluated and verified using theoretical and actual bridge point clouds, respectively. The reconstruction quality of BIM is also evaluated visually and quantitatively, and the results show that the reconstructed BIM is accurate and reliable.

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.

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