Making 3d Models Using Close-Range Photogrammetry: Comparsion of Cameras and Software

Liba, Natalja

doi:10.5593/sgem2019/2.2/s10.069

Cited by 5 publications

(4 citation statements)

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“…By far the most common area where smartphones are used is the documentation of cultural heritage objects. This applies to museum artifacts [ 25 , 26 ], archaeological objects [ 23 , 24 ], and monuments (understood as single objects or complexes of objects) [ 27 , 28 , 29 , 30 ]. The second most popular field is medical applications.…”

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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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: 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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“…dis(P cal , P des ) ≤ εM (13) where M represents the average diameter length of all ellipses of a GCT. ε is a constant scale factor.…”

Section: The Invariance Of the Pldr For Decoding Point Determinationmentioning

confidence: 99%

“…Before using the coded targets above, it is key to identify the coding sequence number. The identification methods for SCTs were reported publicly and used in the professional photogrammetric software Agisoft Metashape [12,13] to automatically perform image matching and three-dimensional (3D) reality reconstruction in unmanned aerial vehicle (UAV) photogrammetry. The GCTs are mainly used and embedded in an industrial digital close-range photogrammetric system, named the video-simultaneous triangulation and resection system (V-STARS) [14].…”

Section: Introductionmentioning

confidence: 99%

Improved Identification for Point-Distributed Coded Targets with Self-Adaption and High Accuracy in Photogrammetry

et al. 2023

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A robust and effective method for the identification of point-distributed coded targets (IPCT) in a video-simultaneous triangulation and resection system (V-STARS) was reported recently. However, its limitations were the setting of critical parameters, it being non-adaptive, making misidentifications in certain conditions, having low positioning precision, and its identification effect being slightly inferior to that of the V-STARS. Aiming to address these shortcomings of IPCT, an improved IPCT, named I-IPCT, with an adaptive binarization, a more precise ellipse-center localization, and especially an invariance of the point–line distance ratio (PLDR), was proposed. In the process of edge extraction, the adaptive threshold Gaussian function was adopted to realize the acquisition of an adaptive binarization threshold. For the process of center positioning of round targets, the gray cubic weighted centroid algorithm was adopted to realize high-precision center localization. In the template point recognition procedure, the invariant of the PLDR was used to realize the determination of template points adaptively. In the decoding procedure, the invariant of the PLDR was adopted to eliminate confusion. Experiments in indoor, outdoor, and unmanned aerial vehicle (UAV) settings were carried out; meanwhile, sufficient comparisons with IPCT and V-STARS were performed. The results show that the improvements can make the identification approximately parameter-free and more accurate. Meanwhile, it presented a high three-dimensional measurement precision in close-range photogrammetry. The improved IPCT performed equally well as the commercial software V-STARS on the whole and was slightly superior to it in the UAV test, in which it provided a fantastic open solution using these kinds of coded targets and making it convenient for researchers to freely apply the coded targets in many aspects, including UAV photogrammetry for high-precision automatic image matching and three-dimensional real-scene reconstruction.

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“…Satchet [9] used the Direct Linear Transformation (DLT) method to compute the interior and exterior orientation parameters for the digital camera of two mobile phones, NOKIA N82 and CoolPAD 288, a total station TOPCON-GPT7501 was used to measure the control point targets; he found that the Total RMSE from using the camera of NOKIA N82 less than Total RMSE of CoolPAD 288, in the amount of 1.5 mm. Liba et al [10] tested different photogrammetric software and digital cameras, one of them the digital camera of Sony Xperia Z1, the found that the worst result was obtained using Sony Xperia Z1 camera (total error 17.9 mm). Kim et al [11] the potential to use smartphones in photogrammetric UAV systems has been evaluated.…”

Section: Introductionmentioning

confidence: 99%

Digital Model in Close-Range Photogrammetry Using a Smartphone Camera

2021

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Smartphones recently expanded the potential for low-cost close-range photogrammetry for 3D modeling. They enable the simultaneous collection of large amounts of data for a variety of requirements. It is possible to calculate image orientation elements and triangular coordinates in phases as in Relative and Absolute image orientation. This study demonstrates the photogrammetric 3D reconstruction approach that performs on tablets and smartphones as well. Images are taken with smartphone cameras of iPhone 6 and then calibrated automatically using normal calibration model for photogrammetry and computer vision on a PC, depend on Agisoft Lens add-on that imbedded in Agisoft program, and MATLAB camera calibration Toolbox, and by using an oriented bunch of images of chessboard pattern for large point cloud-based picture using matching. The camera calibration results indicate that the calibration processing routines pass without any error, and the accuracy of estimated IOPs was convenient compared with non-metric digital cameras and are more accurate in Agisoft Lens in terms of standard error. For the 3D model, 435 cameras were used, 428 cameras located from 435 are aligned in two photogrammetric software, Agisoft PhotoScan, and LPS. The number of tie points that are used in LPS is 10 tie points, and 4 control points which used to estimate the EOPs, and the number of tie points that are regenerated in Agisoft PhotoScan were 135.605 points, the number of Dense cloud 3,716,912 points are generated, for 3D model a number of 316,253 faces are generated, after processing the tiled model generated (6 levels, 1.25 cm/pix), the generated DEM having (2136×1774/pix), the dimensions of the generated high-resolution orthomosaic are (5520×4494, 4.47 cm/pix). For accuracy assessment, the Xerr. = 0.292 m, Yerr. = 0.38577 m, Zerr.= 0.2889 m, and the total RMS = 0.563 m in the estimated locations of the exterior orientation parameters.

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Making 3d Models Using Close-Range Photogrammetry: Comparsion of Cameras and Software

Cited by 5 publications

References 0 publications

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

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

Improved Identification for Point-Distributed Coded Targets with Self-Adaption and High Accuracy in Photogrammetry

Digital Model in Close-Range Photogrammetry Using a Smartphone Camera

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