Photogrammetric Method to Determine Physical Aperture and Roughness of a Rock Fracture

Torkan, Masoud; Janiszewski, Mateusz; Uotinen, Lauri; Baghbanan, Alireza; Rinne, Mikael

doi:10.3390/s22114165

Cited by 14 publications

(12 citation statements)

References 48 publications

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“…The photos were taken omnidirectionally, with the cameras stationary and the object rotating between the photos with an interval of 10°. This is a popular method for modeling small artifacts [ 25 , 26 , 66 , 69 , 89 ]. There were textured walls in the background of the sculpture, so in the development process, it was necessary to remove the so-called stationary tie points.…”

Section: Methodsmentioning

confidence: 99%

“…Solutions that allow recreating the shape and size of individual parts of the body [ 45 , 46 , 47 , 57 , 58 ] and models for the needs of, e.g., prostheses or plastic surgeries [ 48 , 49 , 51 , 63 ] are predominant here. In the case of geological issues, the analysis of rock porosity and roughness is a common topic [ 65 , 66 ], while in geomorphology it is popular to create digital models of various types of geomorphological forms (e.g., cliffs, caves) [ 70 , 71 ]. In terms of industrial applications, it is worth noting displacement determination [ 78 , 79 ] and utility networks modeling [ 75 , 76 ].…”

Section: Introductionmentioning

confidence: 99%

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A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

Jasińska

Pyka

Pastucha

et al. 2023

Sensors

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Recently, the term smartphone photogrammetry gained popularity. This suggests that photogrammetry may become a simple measurement tool by virtually every smartphone user. The research was undertaken to clarify whether it is appropriate to use the Structure from Motion—Multi Stereo View (SfM-MVS) procedure with self-calibration as it is done in Uncrewed Aerial Vehicle photogrammetry. First, the geometric stability of smartphone cameras was tested. Fourteen smartphones were calibrated on the checkerboard test field. The process was repeated multiple times. These observations were found: (1) most smartphone cameras have lower stability of the internal orientation parameters than a Digital Single-Lens Reflex (DSLR) camera, and (2) the principal distance and position of the principal point are constantly changing. Then, based on images from two selected smartphones, 3D models of a small sculpture were developed. The SfM-MVS method was used, with self-calibration and pre-calibration variants. By comparing the resultant models with the reference DSLR-created model it was shown that introducing calibration obtained in the test field instead of self-calibration improves the geometry of 3D models. In particular, deformations of local concavities and convexities decreased. In conclusion, there is real potential in smartphone photogrammetry, but it also has its limits.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

Jasińska

Pyka

Pastucha

et al. 2023

Sensors

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“…The processed images were imported into the software and the 3D models were reconstructed using the complete SfM-MVS workflow presented in [7]. First, the control points were detected automatically from each image.…”

Section: Photogrammetric Reconstructionmentioning

confidence: 99%

“…The site is first digitized into high-resolution 3D models that are properly scaled and oriented [3,4]. The 3D models can then be used to remotely find and characterize the geometrical properties of rock mass discontinuities using computer-assisted methods, e.g., fracture geometry detection with a clustering-based machine learning tool [5], rock quality designation (RQD) estimation [6], or fracture aperture measurements [7].…”

Section: Introductionmentioning

confidence: 99%

Rapid Photogrammetry with a 360-Degree Camera for Tunnel Mapping

Janiszewski¹,

Torkan²,

Uotinen³

et al. 2022

Remote Sensing

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Structure-from-Motion Multi-View Stereo (SfM-MVS) photogrammetry is a viable method to digitize underground spaces for inspection, documentation, or remote mapping. However, the conventional image acquisition process can be laborious and time-consuming. Previous studies confirmed that the acquisition time can be reduced when using a 360-degree camera to capture the images. This paper demonstrates a method for rapid photogrammetric reconstruction of tunnels using a 360-degree camera. The method is demonstrated in a field test executed in a tunnel section of the Underground Research Laboratory of Aalto University in Espoo, Finland. A 10 m-long tunnel section with exposed rock was photographed using the 360-degree camera from 27 locations and a 3D model was reconstructed using SfM-MVS photogrammetry. The resulting model was then compared with a reference laser scan and a more conventional digital single-lens reflex (DSLR) camera-based model. Image acquisition with a 360-degree camera was 3x faster than with a conventional DSLR camera and the workflow was easier and less prone to errors. The 360-degree camera-based model achieved a 0.0046 m distance accuracy error compared to the reference laser scan. In addition, the orientation of discontinuities was measured remotely from the 3D model and the digitally obtained values matched the manual compass measurements of the sub-vertical fracture sets, with an average error of 2–5°.

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“…Increased requirements for measurement errors can be imposed in mechanical testing of parts and materials [ 9 , 10 , 11 ]. The widespread use of smartphones and the rapid development of digital video cameras have led to the development of specific methods for applying them to photogrammetry tasks [ 12 , 13 , 14 ]. The scales of measurements using close-range photogrammetric systems range from nano- [ 15 ] and microscale [ 16 ] to large scales [ 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

Poroykov,

Pechinskaya,

Shmatko

et al. 2023

Sensors

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Close-range photogrammetry methods are widely used for non-contact and accurate measurements of surface shapes. These methods are based on calculating the three-dimensional coordinates of an object from two-dimensional images using special digital processing algorithms. Due to the relatively complex measurement principle, the accurate estimation of the photogrammetric measurement error is a non-trivial task. Typically, theoretical estimations or computer modelling are used to solve this problem. However, these approaches cannot provide an accurate estimate because it is impossible to consider all factors that influence the measurement results. To solve this problem, we propose the use of physical modelling. The measurement results from the photogrammetric system under test were compared with the results of a more accurate reference measurement method. This comparison allowed the error to be estimated under controlled conditions. The test object was a flexible surface whose shape could vary smoothly over a wide range. The estimation of the measurement accuracy for a large number of different surface shapes allows us to obtain new results that are difficult to obtain using standard approaches. To implement the proposed approach, a laboratory system for the error estimation of close-range photogrammetric measurements was developed. The paper contains a detailed description of the developed system and the proposed technique for a comparison of the measurement results. The error in the reference method, which was chosen to be phasogrammetry, was evaluated experimentally. Experimental testing of the stereo photogrammetric system was performed according to the proposed technique. The obtained results show that the proposed technique can reveal dependencies that may not be detected by standard approaches.

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Photogrammetric Method to Determine Physical Aperture and Roughness of a Rock Fracture

Cited by 14 publications

References 48 publications

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

A Simple Way to Reduce 3D Model Deformation in Smartphone Photogrammetry

Rapid Photogrammetry with a 360-Degree Camera for Tunnel Mapping

An Error Estimation System for Close-Range Photogrammetric Systems and Algorithms

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