Photogrammetry image-based approach for imperfect structure modelling and FE analysis

Woloszyk, Krzysztof; Bielski, Paweł Michał; Garbatov, Y.; Mikulski, Tomasz

doi:10.1016/j.oceaneng.2021.108665

Cited by 14 publications

(6 citation statements)

References 19 publications

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“…Cloudy weather is recommended for outdoor photography, while flat lighting is preferred indoors. Capture a minimum of 20 photos from various angles around the object, with at least 50% overlap and neighboring images [7].In our experiment (Figure 3), 42 surrounding photos were taken. More photos improve the accuracy of the result.…”

Section: Capturing the Objectmentioning

confidence: 99%

Photogrammetry Workflow for Obtaining Low-polygon 3D Models Using Free Software

Romero¹,

Quintana²

2023

Computer Science Research Notes

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Section: Capturing the Objectmentioning

confidence: 99%

Photogrammetry Workflow for Obtaining Low-polygon 3D Models Using Free Software

Romero¹,

Quintana²

2023

Computer Science Research Notes

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“…Removing the background from images has the potential to improve computational time as it reduces the number of features present in the image which will be extracted and then matched. Most current approaches to background removal rely on manual masking of images by the user [7,8]. If the background is static relative to the camera, such as in measurement systems using a rotation stage, this can be exploited to remove the background in an automated way (see [9]).…”

Section: Introductionmentioning

confidence: 99%

Autonomous image background removal for accurate and efficient close-range photogrammetry

Eastwood

Leach

Piano

2022

Meas. Sci. Technol.

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Close-range photogrammetry can be used to reconstruct dense point clouds of an object with very high surface coverage, making it useful for manufacturing metrology tasks such as part inspection and validation. However, compared to competing techniques, data processing times can be slow. In this paper we present a method to autonomously remove the background from the images within a photogrammetric dataset. We show that using masked images directly in the reconstruction results in much lower data processing times, with lower memory utilisation. Furthermore, we show that the point density on the object surface is increased while the number of superfluous background points is reduced. Finally, a set of reconstruction results are compared to a set of tactile coordinate measurements. Reconstructions with the background removed are shown to have a standard deviation in the point to mesh distance of up to 30 µm lower than if the background is not removed. This improvement in standard deviation is likely due to the static background, relative to the object on the rotation stage, causing triangulation errors when points are detected and matched on this background data. The proposed approach is shown to be robust over several example artefacts and can, therefore, be implemented to improve the measurement efficiency and measurement results of photogrammetry coordinate measurement systems.

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“…Since then, photogrammetry and digital image correlation have matured and been applied to the field of experimental solid mechanics [10], aerospace industry [11], 3D shape measurement [12], civil engineering and bridge inspection [13] (for a comprehensive review, the work of Baqersad et al [14] is suggested). Photogrammetry has also been applied in association with numerical modeling techniques (usually the (usually the finite element method) ) to analyze different types of structures; however, in these cases, they have been mostly applied to generate accurate geometric models for the subsequent evaluation of their condition states by means of numerical analysis [15][16][17]. Recently, remote measuring procedures have also been applied to determine displacements in structures [18][19][20][21], providing further advances in the field of non-destructive evaluations and structural health monitoring.…”

Section: Introductionmentioning

confidence: 99%

Photogrammetric Process to Monitor Stress Fields Inside Structural Systems

Honório

Pinto

Hillesheim

et al. 2021

Sensors

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This research employs displacement fields photogrammetrically captured on the surface of a solid or structure to estimate real-time stress distributions it undergoes during a given loading period. The displacement fields are determined based on a series of images taken from the solid surface while it experiences deformation. Image displacements are used to estimate the deformations in the plane of the beam surface, and Poisson’s Method is subsequently applied to reconstruct these surfaces, at a given time, by extracting triangular meshes from the corresponding points clouds. With the aid of the measured displacement fields, the Boundary Element Method (BEM) is considered to evaluate stress values throughout the solid. Herein, the unknown boundary forces must be additionally calculated. As the photogrammetrically reconstructed deformed surfaces may be defined by several million points, the boundary displacement values of boundary-element models having a convenient number of nodes are determined based on an optimized displacement surface that best fits the real measured data. The results showed the effectiveness and potential application of the proposed methodology in several tasks to determine real-time stress distributions in structures.

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Photogrammetry image-based approach for imperfect structure modelling and FE analysis

Cited by 14 publications

References 19 publications

Photogrammetry Workflow for Obtaining Low-polygon 3D Models Using Free Software

Photogrammetry Workflow for Obtaining Low-polygon 3D Models Using Free Software

Autonomous image background removal for accurate and efficient close-range photogrammetry

Photogrammetric Process to Monitor Stress Fields Inside Structural Systems

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