Photogrammetry: Geometric Data from Imagery

Lemmens, M.J.P.M.

doi:10.1007/978-94-007-1667-4_7

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…where i represents the input order of photos; K i represents the intrinsic parameter matrix of the camera that took the i photo; D i represents the radial distortion parameter; (R i |T i ) represents the exterior parameter matrix of the camera taking the i photo (where R i represents the rotation matrix and t i represents the translation matrix); and X j � x j y j z j represents the spatial point. e spatial information of the feature points is obtained by solving the above formula, and the intrinsic parameters are obtained by using calibration boards [23]. e exterior parameters, intrinsic parameters, and spatial information of the traditional SFM algorithm are all estimated by the algorithm, while the exterior parameters and intrinsic parameters of the proposed method are obtained externally.…”

Section: Improved Structure From Motion (Sfm)mentioning

confidence: 99%

Three‐Dimensional Reconstruction of Tunnel Face Based on Multiple Images

Qiu

Jian

Cheng

et al. 2021

Advances in Civil Engineering

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The current geological sketch in tunnel engineering is mainly based on sketches of workers. However, geological sketch drawn by workers always offers fundamental data purely due to its drawing mode. A novel drawing method for geological sketch has been introduced using multiview photos in this process. The images of tunnel faces are taken from multiple angles, and every two pictures have overlaps. By measuring the distance between the camera and the tunnel face using a laser range finder, the photographic scale of each photo can be confirmed. SpeededUp Robust Features (SURF) is a good practice for detecting feature points, and the sparse point cloud is reconstructed from multiview photos by structure from motion (SFM). However, the sparse point cloud is not suitable for analysis for structural planes due to its sparsity. Therefore, patch-based multiview stereo (PMVS) is used to reconstruct dense point cloud from the sparse point cloud. After 3D reconstruction, the details of the tunnel face are recorded. The proposed technique was applied to multiview photos acquired in the Xiaosanxia railway tunnel and Fengjie tunnel in Chongqing, China. In order to record the geological conditions of the tunnel face quickly and accurately, Chengdu Tianyou Tunnelkey has developed a set of software and hardware integration system called CameraPad. Besides, CameraPad was used to collect the multiview photos of the tunnel face in the No. 1 Xinan railway tunnel in Jilin, China. By comparing with traditional and existing methods, the proposed method offers a more reductive model for geological conditions of the tunnel face.

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Section: Improved Structure From Motion (Sfm)mentioning

confidence: 99%

Three‐Dimensional Reconstruction of Tunnel Face Based on Multiple Images

Qiu

Jian

Cheng

et al. 2021

Advances in Civil Engineering

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“…The works touching the influence of the distance-baseline ratio over the photogrammetric outcome mostly apply to aerial photogrammetry from drones, where the images are mainly nadiral (i.e., with the cameras' optical axes orthogonal to the terrain and parallel to each other), where the effects of lateral displacement for a given focal length were evaluated globally in terms of image overlap. According to a traditional vision, the overlap between adjacent images in the aerial strips was set to 60%, as forward overlap (along-track), while the overlap between adjacent images between strips was set to 20% as side overlap (across track) [33,34]. However, Haala and Rothermel in 2012 [35], analyzing a UAV survey by nadiral images taken with a zoom camera Canon Ixus 100 IS with the minimal zoom setting (f = 5.9 mm) at a height of about 115 m (GSD = 30 mm), tried to experiment larger overlap values.…”

Section: Previous Work About Optimal Cameras Orientation In Sfm/im Photogrammetrymentioning

confidence: 99%

Optimal Lateral Displacement in Automatic Close-Range Photogrammetry

Guidi

Malik

Micoli

2020

Sensors

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Based on the use of automatic photogrammetry, different researchers made evident that the level of overlap between adjacent photographs directly affects the uncertainty of the 3D dense cloud originated by the Structure from Motion/Image Matching (SfM/IM) process. The purpose of this study was to investigate if, in the case of a convergent shooting typical of close-range photogrammetry, an optimal lateral displacement of the camera for minimizing the 3D data uncertainty could be identified. We examined five different test objects made of rock, differing in terms of stone type and visual appearance. First, an accurate reference data set was generated by acquiring each object with an active range device, based on pattern projection (σz = 18 µm). Then, each object was 3D-captured with photogrammetry, using a set of images taken radially, with the camera pointing to the center of the specimen. The camera–object minimum distance was kept at 200 mm during the shooting, and the angular displacement was as small as π/60. We generated several dense clouds by sampling the original redundant sequence at angular displacements (nπ/60, n=1, 2, … 8). Each 3D cloud was then compared with the reference, implementing an accurate scaling protocol to minimize systematic errors. The residual standard deviation of error made consistently evident a range of angular displacements among images that appear to be optimal for reducing the measurement uncertainty, independent of each specimen shape, material, and texture. Such a result provides guidance about how best to arrange the cameras’ geometry for 3D digitization of a stone cultural heritage artifact with several convergent shots. The photogrammetric tool used in the experiments was Agisoft Metashape.

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Photogrammetry: Geometric Data from Imagery

Cited by 2 publications

References 8 publications

Three‐Dimensional Reconstruction of Tunnel Face Based on Multiple Images

Three‐Dimensional Reconstruction of Tunnel Face Based on Multiple Images

Optimal Lateral Displacement in Automatic Close-Range Photogrammetry

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