Using open-source software for extracting geomechanical parameters of a rock mass from 3D point clouds: Discontinuity set extractor and SMRTool

Impacts of underground mining have been reduced by continuous environmental endeavors, scientific, and engineering research activities, whose main object is the behavior and control of the undermined rock mass and the subsequent surface subsidence. In the presented Velenje case of underground sublevel longwall mining where coal is being exploited both horizontal and vertical, backfilling processes and accompanying fracturing in the coal layer, and rock mass are causing uncontrolled subsidence of the surface above. 3D point clouds of the study were acquired in ten epochs and at excavation heights on the front were measured at the same epochs. By establishing a sectors layout in the observational area, smaller point clouds were obtained, to which planes were fitted and centroids of these planes then calculated. Centroid heights were analyzed with the FNSE model to estimate the time of consolidation and modified according to excavation parameters to determine total subsidence after a certain period. Proposed prognosis approaches for estimating consolidation of active subsidence and long term surface environmental protection measures have been proposed and presented. The C2C analysis of distances between acquired 3D point clouds was used for identification of surface subsidence, reclamation areas and sink holes, and for validation of feasibility and effectiveness of the proposed prognosis.

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Automatic characterization of rock mass discontinuities using 3D point clouds

Engineering Geology ◽

10.1016/j.enggeo.2019.05.008 ◽

2019 ◽

Vol 259 ◽

pp. 105131 ◽

Cited By ~ 6

Author(s):

Xiaojun Li ◽

Ziyang Chen ◽

Jianqin Chen ◽

Hehua Zhu

Keyword(s):

Rock Mass ◽

Point Clouds ◽

3D Point Clouds

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OVERVIEW OF OPEN SOURCE SOFTWARE FOR CLOSE RANGE PHOTOGRAMMETRY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w14-239-2019 ◽

2019 ◽

Vol XLII-4/W14 ◽

pp. 239-245 ◽

Cited By ~ 3

Author(s):

G. Vacca

Keyword(s):

Open Source ◽

Camera Calibration ◽

Open Source Software ◽

Point Cloud ◽

Ground Level ◽

Point Clouds ◽

Field Analysis ◽

Full Field ◽

Close Range ◽

Image Orientation

Abstract. In the photogrammetric process of the 3D reconstruction of an object or a building, multi-image orientation is one of the most important tasks that often include simultaneous camera calibration. The accuracy of image orientation and camera calibration significantly affects the quality and accuracy of all subsequent photogrammetric processes, such as determining the spatial coordinates of individual points or 3D modeling. In the context of artificial vision, the full-field analysis procedure is used, which leads to the so-called Strcture from Motion (SfM), which includes the simultaneous determination of the camera's internal and external orientation parameters and the 3D model. The procedures were designed and developed by means of a photogrammetric system, but the greatest development and innovation of these procedures originated from the computer vision from the late 90s, together with the SfM method. The reconstructions on this method have been useful for visualization purposes and not for photogrammetry and mapping. Thanks to advances in computer technology and computer performance, a large number of images can be automatically oriented in a coordinate system arbitrarily defined by different algorithms, often available in open source software (VisualSFM, Bundler, PMVS2, CMVS, etc.) or in the form of Web services (Microsoft Photosynth, Autodesk 123D Catch, My3DScanner, etc.). However, it is important to obtain an assessment of the accuracy and reliability of these automated procedures. This paper presents the results obtained from the dome low close range photogrammetric surveys and processed with some open source software using the Structure from Motion approach: VisualSfM, OpenDroneMap (ODM) and Regard3D. Photogrammetric surveys have also been processed with the Photoscan commercial software by Agisoft.For the photogrammetric survey we used the digital camera Canon EOS M3 (24.2 Megapixel, pixel size 3.72&thinsp;mm). We also surveyed the dome with the Faro Focus 3D TLS. Only one scan was carried out, from ground level, at a resolution setting of &frac14; with 3x quality, corresponding to a resolution of 7&thinsp;mm / 10&thinsp;m. Both TLS point cloud and Photoscan point cloud were used as a reference to validate the point clouds coming from VisualSFM, OpenDroneMap and Regards3D. The validation was done using the Cloud Compare open source software.

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A new approach for semi-automatic rock mass joints recognition from 3D point clouds

Computers & Geosciences ◽

10.1016/j.cageo.2014.03.014 ◽

2014 ◽

Vol 68 ◽

pp. 38-52 ◽

Cited By ~ 102

Author(s):

Adrián J. Riquelme ◽

A. Abellán ◽

R. Tomás ◽

M. Jaboyedoff

Keyword(s):

Rock Mass ◽

Point Clouds ◽

New Approach ◽

3D Point Clouds

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

Remote Sensing ◽

10.3390/rs13152894 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2894

Author(s):

Xiang Wu ◽

Fengyan Wang ◽

Mingchang Wang ◽

Xuqing Zhang ◽

Qing Wang ◽

...

Keyword(s):

Rock Mass ◽

Clustering Algorithm ◽

Point Clouds ◽

New Method ◽

Rock Masses ◽

Average Deviation ◽

Density Peaks ◽

3D Point Clouds ◽

Discontinuity Sets ◽

Dip Angle

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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GENERATING ACCURATE 3D MODELS OF ARCHITECTURAL HERITAGE STRUCTURES USING LOW-COST CAMERA AND OPEN SOURCE ALGORITHMS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-w1-99-2017 ◽

2017 ◽

Vol XLII-5/W1 ◽

pp. 99-104

Author(s):

M. Zacharek ◽

P. Delis ◽

M. Kedzierski ◽

A. Fryskowska

Keyword(s):

Open Source ◽

Open Source Software ◽

Web Application ◽

Low Cost ◽

Digital Camera ◽

Point Clouds ◽

3D Models ◽

Dense Image ◽

Dense Point ◽

Historical Heritage

These studies have been conductedusing non-metric digital camera and dense image matching algorithms, as non-contact methods of creating monuments documentation.In order toprocess the imagery, few open-source software and algorithms of generating adense point cloud from images have been executed. In the research, the OSM Bundler, VisualSFM software, and web application ARC3D were used. Images obtained for each of the investigated objects were processed using those applications, and then dense point clouds and textured 3D models were created. As a result of post-processing, obtained models were filtered and scaled.The research showedthat even using the open-source software it is possible toobtain accurate 3D models of structures (with an accuracy of a few centimeters), but for the purpose of documentation and conservation of cultural and historical heritage, such accuracy can be insufficient.

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CLASSIFICATION AND INTEGRATION OF MASSIVE 3D POINTS CLOUDS IN A VIRTUAL REALITY (VR) ENVIRONMENT

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w17-165-2019 ◽

2019 ◽

Vol XLII-2/W17 ◽

pp. 165-171 ◽

Cited By ~ 3

Author(s):

A. Kharroubi ◽

R. Hajji ◽

R. Billen ◽

F. Poux

Keyword(s):

Virtual Reality ◽

Open Source ◽

Point Cloud ◽

Mixed Reality ◽

Automatic Segmentation ◽

Point Clouds ◽

3D Point Clouds ◽

3D Data ◽

Segmentation Approach ◽

And Performance

Abstract. With the increasing volume of 3D applications using immersive technologies such as virtual, augmented and mixed reality, it is very interesting to create better ways to integrate unstructured 3D data such as point clouds as a source of data. Indeed, this can lead to an efficient workflow from 3D capture to 3D immersive environment creation without the need to derive 3D model, and lengthy optimization pipelines. In this paper, the main focus is on the direct classification and integration of massive 3D point clouds in a virtual reality (VR) environment. The emphasis is put on leveraging open-source frameworks for an easy replication of the findings. First, we develop a semi-automatic segmentation approach to provide semantic descriptors (mainly classes) to groups of points. We then build an octree data structure leveraged through out-of-core algorithms to load in real time and continuously only the points that are in the VR user's field of view. Then, we provide an open-source solution using Unity with a user interface for VR point cloud interaction and visualisation. Finally, we provide a full semantic VR data integration enhanced through developed shaders for future spatio-semantic queries. We tested our approach on several datasets of which a point cloud composed of 2.3 billion points, representing the heritage site of the castle of Jehay (Belgium). The results underline the efficiency and performance of the solution for visualizing classifieds massive point clouds in virtual environments with more than 100 frame per second.

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OpenDroneMap360, an affordable DIY open-source hardware and software workflow for 3D point clouds and terrain models

10.5194/egusphere-egu21-16254 ◽

2021 ◽

Author(s):

Hessel Winsemius ◽

Stephen Mather ◽

Ivan Gayton ◽

Iddy Chazua

Keyword(s):

Open Source ◽

Low Income ◽

Urban Areas ◽

Service Providers ◽

Point Clouds ◽

Low Income Countries ◽

Raspberry Pi ◽

Data Generation ◽

3D Point Clouds ◽

Open Source Hardware

The state of the art in terrain data generation is Light Detection And Ranging (LiDAR). LiDAR is usually deployed through manned or unmanned aerial vehicles. As typical payloads are high, an aircraft with LiDAR needs to be significant in size. Therefore, LiDAR is currently only done by specialized companies with expensive equipment, and cannot be deployed by local service providers in low income countries, despite the plethora of use cases for its data.&#160;A promising avenue to replace LiDAR is photogrammetry. It can be applied with much lighter and more affordable aircrafts and its use to provide extensive terrain datasets is steadily increasing. The scalable open-source software OpenDroneMap allows for extending datasets to very large amounts. Photogrammetry however, cannot penetrate vegetation, and (as is the case with LiDAR) does not resolve ground terrain in obscured areas such as dense urban areas with narrow alleys.&#160;That is why we are developing OpenDroneMap360, a free and open-source DIY hardware-software camera-ball platform for collection of high quality photos with any carrier you can think of. This can be a self-built drone, a backpack rig or another setup we haven&#8217;t considered yet, equipped with enough lenses to discover any ground that you can think of. Our current hardware offers a backpack rig with a total of 7 lenses and contains a parts list, 3D-printable hull, connection scheme, software deployment and a Sphinx manual how to build, deploy and operate the rig. The technology contains raspberry pi cameras connected to raspberry pi zeros for each lens, a Ardusimple u-blox ZED-F9P GNSS chipset, a raspberry pi4 to instruct the cameras, collect GPS positions, and perform file and data management, and a LiPo battery solution. The entire setup is available on https://github.com/localdevices/odm360&#160;

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