Three-dimensional Reconstruction Method Research Articles

Method for three-dimensional reconstruction of dynamic stereo vision based on line structured light using global optimization

To overcome the limitation of the measurement range of static stereo vision, a three-dimensional (3-D) reconstruction method of dynamic stereo vision based on line structured light for metal surface is presented in this paper. The dynamic stereo vision is mainly composed of binocular cameras and a laser line generator, and moves along a horizontal guide rail. The intrinsic parameters of the cameras and the rotation and translation of the right camera relative to the left camera are previously obtained by Zhang’s method. To obtain the 3-D coordinates of the line structured light fringe in each position, the sub-pixel coordinates of the fringe center are extracted by Steger algorithm, and a joint calibration method between single camera and a laser line generator is proposed. The joint calibration method calculates the plane equation of the line structured light by using multiple checkerboard images with projected structured light line. To unify the 3-D coordinates of structured light fringe in each position to the reference coordinate system, the rotation and translation of the camera in each position relative to the reference position (called absolute pose) should be solved. The direct linear transformation method is firstly used to calculate the rough relative pose between adjacent frames, and principle of re-projection error minimization in object space is used to obtain the refined relative pose. Instead of using step-by-step transfer method, the absolute pose of the camera in each position is eventually calculated by using global optimization algorithm after the relative pose between all adjacent frames are obtained. Computer simulation and abundant experimental data validate the effectiveness of proposed method. The proposed 3-D reconstruction method using global optimization is respectively compared with step-by-step transfer method based on pixel re-projection error minimization and re-projection error minimization in object space. The experimental results reflect that our method is superior to step-by-step transfer method, and the 3-D reconstruction accuracy is improved by 28.05 % after global optimization.

3D reconstruction of nonuniform and unsteady flow velocity distribution using nonlinear acoustic ray tracing and tomography

The nonuniform and unstable gas flow distribution at the air inlets of the boiler combustion chamber can negatively affect the energy efficiency and safety of the boiler. Accurately reconstructing the flow velocity inside the furnace in three dimensions can help the boiler automatically adjust the air supply to reduce unwanted energy waste and gas emissions.In this study, we propose a novel method for three-dimensional velocity reconstruction based on nonlinear acoustic ray tracing and tomography. The model provides the equation of acoustic ray motion in the flow field, which includes spatial and temporal parameters. During the process of measuring flow velocity, higher precision reconstruction of the three-dimensional velocity distribution was achieved by combining acoustic refractive paths based on derivative-free optimization iteration updates with radial basis functions that use Gaussian kernels.The computational time cost and reconstruction accuracy of the proposed algorithm were analyzed numerically and through simulations. The results indicate that the method incorporating acoustic ray tracing greatly improves the reconstruction accuracy of large gradient nonuniform flow fields. Additionally, we analyzed the impact of different numbers of sound tracing paths on reconstruction quality and computation time, providing a reference for the rational placement of microphones and the selection of the most efficient paths in boilers.Experimental results show that this method has good reconstruction capabilities for nonuniform flow fields and practical applications.

A refractive three-dimensional reconstruction method for fringe projection with a planar medium

In special measurement situations, a vision measurement system needs to measure objects through a viewing observation window made of a planar transparent medium. The refraction at the interface between a medium and air leads to large measurement errors in the pin-hole imaging model. Most existing solutions require complex processes of calculating refracted light equations and/or matching stereo images. Here, we propose a refractive three-dimensional (3D) reconstruction method for fringe projection with a planar medium. We derive a coordinate conversion relationship from a pseudo-object point caused by light refraction to a real-object point based on flat refractive geometry. Then, we integrate the relationship into regular fringe projection framework for unbiased 3D reconstruction. Two experiments, including 3D shape measurements of a step and a regular sphere are performed. The results verify the effectiveness and accuracy of the proposed refractive 3D reconstruction method.

Three-Dimensional Point Cloud Reconstruction Method of Cardiac Soft Tissue Based on Binocular Endoscopic Images

Three-dimensional reconstruction technology based on binocular stereo vision is a key research area with potential clinical applications. Mainstream research has focused on sparse point reconstruction within the soft tissue domain, limiting the comprehensive 3D data acquisition required for effective surgical robot navigation. This study introduces a new paradigm to address existing challenges. An innovative stereoscopic endoscopic image correction algorithm is proposed, exploiting intrinsic insights into stereoscopic calibration parameters. The synergy between the stereoscopic endoscope parameters and the disparity map derived from the cardiac soft tissue images ultimately leads to the acquisition of precise 3D points. Guided by deliberate filtering and optimization methods, the triangulation process subsequently facilitates the reconstruction of the complex surface of the cardiac soft tissue. The experimental results strongly emphasize the accuracy of the calibration algorithm, confirming its utility in stereoscopic endoscopy. Furthermore, the image rectification algorithm exhibits a significant reduction in vertical parallax, which effectively enhances the stereo matching process. The resulting 3D reconstruction technique enables the targeted surface reconstruction of different regions of interest in the cardiac soft tissue landscape. This study demonstrates the potential of binocular stereo vision-based 3D reconstruction techniques for integration into clinical settings. The combination of joint calibration algorithms, image correction innovations, and precise tissue reconstruction enhances the promise of improved surgical precision and outcomes in the field of cardiac interventions.

A low-cost 3D reconstruction and measurement system based on structure-from-motion (SFM) and multi-view stereo (MVS) for sewer pipelines

Traditional image-based detection methods for pipelines lack quantification information (e.g., depth, area, and perimeter of defects) despite the high accuracy. Furthermore, existing three-dimensional (3D) reconstruction methods based on laser and depth cameras are expensive and not maneuverable. To remedy these problems, a simple and novel measurement system for sewer pipeline potholes based on low-cost 3D reconstruction is proposed. First, a sparse reconstruction method based on structure-from-motion (SFM) is proposed to estimate the camera parameters and reconstruct the sparse point cloud from multi-view images that are easily acquired. Second, a dense reconstruction method based on multi-view stereo (MVS) is proposed to generate the depth maps and dense 3D points that can provide a stereo display of pipelines. Third, an automatic segmentation and measurement method based on cylinder fitting and projection for pipeline potholes is proposed. The measurement information of potholes is obtained by projection, triangulation and boundary search. Furthermore, a metric calibration method is proposed to convert the voxel size to the actual size. Comparison experiments show that the average errors of maximum depths, mean depths, areas and perimeters between the predicted and real values are 9.48 %, 13.16 %, 6.70 % and 14.01 %, respectively. Furthermore, the measurement method is robust to the point density of the potholes. The whole proposed system only requires several overlapping images taken from ordinary cameras, which is a low-cost and accurate way for 3D reconstruction of pipelines and measurement of defects.

3D biplanar reconstruction of lower limbs using nonlinear statistical models.

Three-dimensional (3D) reconstruction of lower limbs is of great interest in surgical planning, computer assisted surgery, and for biomechanical applications. The use of 3D imaging modalities such as computed tomography (CT) scan and magnetic resonance imaging (MRI) has limitations such as high radiation and expense. Therefore, three-dimensional reconstruction methods from biplanar X-ray images represent an attractive alternative. In this paper, we present a new unsupervised 3D reconstruction method for the patella, talus, and pelvis using calibrated biplanar (45- and 135-degree oblique) radiographic images and a prior information on the geometric/anatomical structure of these complex bones. A multidimensional scaling (MDS)-based nonlinear dimensionality reduction algorithm is applied to exploit this prior geometric/anatomical information. It represents relevant deformations existing in the training set. Our method is based on a hybrid-likelihood using regions and contours. The edge-based notion represents the relation between the external contours of the bone projections and an edge potential field estimated on the radiographic images. Region-based notion is the non-overlapping ratio between segmented and projected bone regions of interest (RoIs). Our automatic 3D reconstruction model entails stochastically minimizing an energy function allowing an estimation of deformation parameters of the bone shape. This 3D reconstruction method has been successfully tested on 13 biplanar radiographic image pairs, yielding very promising results.

Extraction of soybean plant trait parameters based on SfM-MVS algorithm combined with GRNN.

Soybean is an important grain and oil crop worldwide and is rich in nutritional value. Phenotypic morphology plays an important role in the selection and breeding of excellent soybean varieties to achieve high yield. Nowadays, the mainstream manual phenotypic measurement has some problems such as strong subjectivity, high labor intensity and slow speed. To address the problems, a three-dimensional (3D) reconstruction method for soybean plants based on structure from motion (SFM) was proposed. First, the 3D point cloud of a soybean plant was reconstructed from multi-view images obtained by a smartphone based on the SFM algorithm. Second, low-pass filtering, Gaussian filtering, Ordinary Least Square (OLS) plane fitting, and Laplacian smoothing were used in fusion to automatically segment point cloud data, such as individual plants, stems, and leaves. Finally, Eleven morphological traits, such as plant height, minimum bounding box volume per plant, leaf projection area, leaf projection length and width, and leaf tilt information, were accurately and nondestructively measured by the proposed an algorithm for leaf phenotype measurement (LPM). Moreover, Support Vector Machine (SVM), Back Propagation Neural Network (BP), and Back Propagation Neural Network (GRNN) prediction models were established to predict and identify soybean plant varieties. The results indicated that, compared with the manual measurement, the root mean square error (RMSE) of plant height, leaf length, and leaf width were 0.9997, 0.2357, and 0.2666 cm, and the mean absolute percentage error (MAPE) were 2.7013%, 1.4706%, and 1.8669%, and the coefficients of determination (R2) were 0.9775, 0.9785, and 0.9487, respectively. The accuracy of predicting plant species according to the six leaf parameters was highest when using GRNN, reaching 0.9211, and the RMSE was 18.3263. Based on the phenotypic traits of plants, the differences between C3, 47-6 and W82 soybeans were analyzed genetically, and because C3 was an insect-resistant line, the trait parametes (minimum box volume per plant, number of leaves, minimum size of single leaf box, leaf projection area).The results show that the proposed method can effectively extract the 3D phenotypic structure information of soybean plants and leaves without loss which has the potential using ability in other plants with dense leaves.

A Novel Three-Dimensional Reconstruction Technology for the Defect Inspection of Tubing and Casing

The three-dimensional reconstruction of high-gloss/reflection and low-texture objects (e.g., oil casing threads) is a complex task. In this paper, we present a novel approach that combines convolutional neural networks (CNNs) and multi-layer perception (MLP) with traditional three-dimensional reconstruction methods, thereby enhancing the detection efficiency. Our method utilizes a dataset of 800 samples that includes a variety of thread defects to train a U-net-like model as a three-dimensional reconstructor. Then, an MLP model is proposed to improve the accuracy of the three-dimensional reconstructed thread profile to the level of three-coordinate measurements through a regression analysis. The experimental results demonstrate that the method can effectively detect the black-crested threads of oil casing threads and quantify their proportions in the entire sample for accurate quality assessment. The method is easy to operate and can detect black threads effectively, providing a powerful tool for oil companies to ensure exploration benefits.

Dynamic evaluation of microscopic damage and fluid flow behavior in reservoir shale under deviatoric stress

This study presented a three-dimensional reconstruction method for digital shale cores in detail and examined the impact of deviatoric stress on the microscopic damage and fluid flow behavior in reservoir shale under deviatoric stress. In particular, this paper investigated the distribution of damage, deformation, and stress in shale under different deviatoric stresses. In addition, the characteristics of fluid velocity and pressure distribution under deviatoric stress and permeability evolution were discussed. The results showed that: (1) The deformation and damage in digital cores increased with the deviatoric stress, and the pore area will be more affected by deviatoric stress than the matrix area. (2) The fluid velocity distribution varies significantly between the matrix and the pore area, and the flaky connected fracture will aggravate this difference. (3) The fluid velocities of digital cores D1 and D2 decreased first and then increased with the increase of the deviatoric stress. (4) The permeability of digital cores D1 and D2, as the deviatoric stress increased, initially decreased due to rock deformation but subsequently increased due to the increasing damage.

Hybrid iteration and optimization-based three-dimensional reconstruction for space non-cooperative targets with monocular vision and sparse lidar fusion

The measurement of reference pose and geometric size for space non-cooperative targets is an essential premise of on-orbit servicing. In this paper, a three-dimensional (3-D) reconstruction and autonomous geometric parameter identification method is proposed. An autonomous region segmentation algorithm with monocular vision and sparse lidar fusion is introduced, which realizes the multi-source match of rich features in images and scale information in point clouds. In order to densify the sparse point cloud, a multi-view fusion model is constructed with multi-objective optimization, which realizes the complementation of 3-D information. Further, a 3-D reconstruction method with hybrid iteration and optimization is proposed. Unlike the method with random sampling, the proposed method can obtain the optimal solution while reducing the influence of outliers. In other to verify the effectiveness of the proposed method, a complete set of simulation and experimental systems with modular and scalable software is built. The results show that the accuracy and stability are improved by no less than 27.88% and 38.99%, respectively. Besides, the inference speed is enhanced by at least one time. The proposed method can accurately and quickly identify the geometric parameters of targets with a cubical body, which will be used for motion estimation. Moreover, it can be extended to targets with a polyhedral body.

Scan-free time-of-flight-based three-dimensional imaging through a scattering layer.

Reconstructing an object's three-dimensional shape behind a scattering layer with a single exposure is of great significance in real-life applications. However, due to the little information captured by a single exposure while strongly perturbed by the scattering layer and encoded by free-space propagation, existing methods cannot achieve scan-free three-dimensional reconstruction through the scattering layer in macroscopic scenarios using a short acquisition time of seconds. In this paper, we proposed a scan-free time-of-flight-based three-dimensional reconstruction method based on explicitly modeling and inverting the time-of-flight-based scattering light propagation in a non-confocal imaging system. The non-confocal time-of-flight-based scattering imaging model is developed to map the three-dimensional object shape information to the time-resolved measurements, by encoding the three-dimensional object shape into the free-space propagation result and then convolving with the scattering blur kernel derived from the diffusion equation. To solve the inverse problem, a three-dimensional shape reconstruction algorithm consisting of the deconvolution and diffractive wave propagation is developed to invert the effects caused by the scattering diffusion and the free-space propagation, which reshapes the temporal and spatial distribution of scattered signal photons and recovers the object shape information. Experiments on a real scattering imaging system are conducted to demonstrate the effectiveness of the proposed method. The single exposure used in the experiment only takes 3.5s, which is more than 200 times faster than confocal scanning methods. Experimental results show that the proposed method outperforms existing methods in terms of three-dimensional reconstruction accuracy and imaging limit subjectively and objectively. Even though the signal photons captured by a single exposure are too highly scattered and attenuated to present any valid information in time gating, the proposed method can reconstruct three-dimensional objects located behind the scattering layer of 9.6 transport mean free paths (TMFPs), corresponding to the round-trip scattering length of 19.2 TMFPs.

Hierarchical 3D reconstruction algorithm of core image based on detailed and structural dictionaries

The microstructure of porous media is of great significance to the study and analysis of their physical properties and mechanical properties. At present, the three-dimensional reconstruction method is widely used to obtain digital models of porous media microstructures that can be used to reconstruct their three-dimensional structures from two-dimensional training images. The super-dimensional algorithm, a learning-based method, makes full use of a priori three-dimensional structural information, and its reconstruction effect is better than that of the aforementioned traditional method. However, most current algorithms are based on 128- or 256-size training image reconstruction, which is still far from the actual application of the image size. To make algorithms more suitable to the actual application, we herein propose a hierarchical super-dimensional reconstruction method based on the idea of a multiple grid to complete the reconstruction of large-size images, such as the three-dimensional reconstruction of 512- or even 1000-size images. Using this method, we designed a template of the detailed information dictionary and developed a corresponding matching algorithm for a hierarchical reconstruction process. To further improve the efficiency of reconstruction, dictionary element porosity characteristics were used for classification. Finally, to verify the effectiveness of the proposed method, four groups of sandstone image reconstruction experiments were carried out, and the reference images with different sizes and porosity were reconstructed several times. Furthermore, to determine the accuracy of the reconstruction results, we compared the real structure with the visual and statistical indicators, and pore and throat parameters.

Preliminary study on three-dimensional morphological reconstruction method for external nose defect based on three-dimensional face template

Objective: To provide a new solution for the digital design of nasal prostheses, this study explores the three-dimensional (3D) facial morphology completion method for external nasal defects based on the non-rigid registration process of 3D face template. Methods: A total of 20 male patients with tooth defect and dentition defect who visited the Department of Prosthodontics, Peking University School and Hospital of Stomatology from June to December 2022 were selected, age 18-45 years old. The original 3D facial data of patients were collected, and the 3D facial data of the external nose defect was constructed in Geomagic Wrap 2021 software. Using the structured 3D face template data constructed in the previous research of the research group, the 3D face template was deformed and registered to the 3D facial data of external nose defect (based on the morphology of non-defective area) by non-rigid registration algorithm (MeshMonk program), and the personalized deformed data of the 3D face template was obtained, as the complemented facial 3D data. Based on the defect boundary of the 3D facial data of the external nose defect, the complemented external nose 3D data can be cut out from the complemented facial 3D data. Then the nasofacial angle and nasolabial angle of the complemented facial 3D data and the original 3D facial data was compared and analyzed, the ratio between the nose length and mid-face height, nose width and medial canthal distance of the complemented facial 3D data was measured, the edge fit between the edge curve of the complemented external nose 3D data and the defect edge curve of the 3D facial data of external nose defect was evaluated, and the morphological difference of the nose between the complemented external nose 3D data and the original 3D facial data was analyzed. Results: There was no significant statistically difference (t=-0.23, P=0.823; Z=-1.72, P=0.086) in the nasofacial angle (28.2°±2.9°, 28.4°±3.5° respectively) and nasolabial angle [95.4°(19.2°), 99.9°(9.5°) respectively] between the 20 original 3D facial data and the complemented facial 3D data. The value of the ratio of nose length to mid-face height in the complemented facial 3D data was 0.63±0.03, and the value of the ratio of nose width to medial canthal distance was 1.07±0.08. The curve deviation (root mean square value) between the edge curve of the complemented external nose 3D data and the defect edge curve of the 3D facial data of external nose defect was (0.37±0.09) mm, the maximum deviation was (1.14±0.32) mm, and the proportion of the curve deviation value within±1 mm was (97±3)%. The distance of corresponding nose landmarks between the complemented facial 3D data and the original 3D facial data were respectively, Nasion: [1.52(1.92)] mm; Pronasale: (3.27±1.21) mm; Subnasale: (1.99±1.09) mm; Right Alare: (2.64±1.34) mm; Left Alare: (2.42± 1.38) mm. Conclusions: The method of 3D facial morphology completion of external nose defect proposed in this study has good feasibility. The constructed complemented external nose 3D data has good facial coordination and edge fit, and the morphology is close to the nose morphology of the original 3D facial data.

Long-Term Effect Analysis of a High Slope with Tunnel Structure Based on Three-Dimensional Mesh Reconstruction

The instability of high rock slopes with tunnel structures increases under the effects of earthquakes and rainfall. Limited studies have been conducted on the long-term effect analysis of high rock slopes with tunnels. Field electrical measurements on rock slopes have been conducted to study the response of high rock slopes with large cross-section tunnels during rainfall and seismic loading. The material threshold was determined using the resistivity probability density statistical method, and a three-dimensional geological visualization model was obtained. A three-dimensional mesh reconstruction method consisting of material segmentation, cluster filtering, mesh generation, and material attribute mapping modules was proposed. The three-dimensional model of high-cutting rock slope was reconstructed, and the three-dimensional finite element model of the rock slope with tunnel was obtained. The response analysis of the slope was performed during rainfall and seismic loading by employing the El-Centro wave. The results demonstrated that plastic strain was generated in the fracture zone of the upper part of the tunnel during rainfall, and the strain value was 8.135 × 10−4. In the subsequent stages, it had a tendency to expand into the tunnel under continuous rainfall. The maximum principal strain was 3.324 × 10−2 under the effect of a strong earthquake. A large strain was generated in the fracture zone at the upper part of the tunnel. It had a tendency to expand under long-term load, which significantly affected the safety of the tunnel.

GPU-accelerated three-dimensional reconstruction method of the Compton camera and its application in radionuclide imaging

GPU-accelerated three-dimensional reconstruction method of the Compton camera and its application in radionuclide imaging

Three-Dimensional Geometry Reconstruction Method from Multi-View ISAR Images Utilizing Deep Learning

The three-dimensional (3D) geometry reconstruction method utilizing ISAR image sequence energy accumulation (ISEA) shows great performance on triaxial stabilized space targets but fails when there is unknown motion from the target itself. The orthogonal factorization method (OFM) can solve this problem well under certain assumptions. However, due to the sparsity and anisotropy of ISAR images, the extraction and association of feature points become very complicated, resulting in the reconstructed geometry usually being a relatively sparse point cloud. Therefore, combining the advantages of the above methods, an extended factorization framework (EFF) is proposed. First, the instance segmentation method based on deep learning is used for the extraction and association of a number of key points on multi-view ISAR images. Then, the projection vectors between the 3-D geometry of the space target and the multi-view ISAR images are obtained, using the improved factorization method. Finally, the 3D geometry reconstruction problem is transformed into an unconstrained optimization problem and solved via the quantum-behaved particle swarm optimization (QPSO) method. The proposed framework uses discretely observed multi-view range–Doppler ISAR images as an input, which can make full use of the long-term data of space targets from multiple perspectives and which is non-sensitive to movement. Therefore, the proposed framework shows high feasibility in practical applications. Experiments on simulated and measured data show the effectiveness and robustness of the proposed framework.

Automated image-based geometric modelling of thick laminates with three-dimensional ply wrinkles

Ply wrinkles are commonly induced during thermal–mechanical manufacturing process of laminated composite structures or components. Fibre misalignment due to ply wrinkles compromises the structural integrity of a composite structure. The damage criticality can be assessed numerically based on non-destructive testing results. Geometric modelling is usually required and it is time-consuming if complex wrinkles have to be incorporated into the finite element model. This study presents an automated image-based three-dimensional geometric reconstruction method for thick laminates with complex ply wrinkles. The geometry of a thick laminate is reconstructed based on the limited inspection images from the sides of laminates. The two-dimensional geometric reconstruction of the detected wrinkled plies is done using a multiple Gaussian functions model or linear interpolation, while the intermediate plies are defined by distance-based interpolation. Based on the reconstructed two-dimensional geometries, the three-dimensional ply wrinkles are modelled via an inverse distance weighting interpolation method. To verify the proposed method, a thick glass fibre laminate with artificially induced wrinkles is manufactured and the cross-sectional geometries of the reconstructed model are compared well with and the realistic inspection data. Progressive failure analysis of a two-dimensional and a three-dimensional reconstructed model are compared in terms of damage progression and load versus nominal strain response to highlight the relevance of modelling three-dimensional wrinkles to obtain more accurate and efficient damage criticality assessments.

Intelligent microscopic 3D shape reconstruction method based on 3D time-frequency transformation

Three-dimensional (3D) shape reconstruction methods based on image focus information generally use a unified focus evaluation metric for the depth-of-field image sequences of microscopic objects. With the absence of the correlation among these sequences, this kind of reconstruction method fails to correct the depth errors of consecutive frame images that result from textural sparsity or low contrast in images. In this paper, different image sequences of microscopic objects are interpreted as 3D image data, such that the correlation among all the sequences can be introduced to the reconstruction. Then, a reconstruction framework encompassing multiview analysis, locally stable clustering, and selective fusion as its key components is established from the perspective of 3D time-frequency transformation. To be specific, we analyze the advantage of 3D image data in shape reconstruction over general 2D image data and construct a 3D time-frequency transform that maps a 3D image to depth images, which contain information of varying scales, regions, and directions. Next, to enhance depth image features, a locally stable clustering algorithm is constructed based on multimodal textural features, which improves the adaptive selection ability of depth images with good homogeneity. Finally, we propose the selective fusion of depth images, which outputs accurate 3D morphology results by fitting multiple layers of noise-free depth images through a layer-by-layer sifting balance tree designed in this study. The effectiveness of the proposed method is verified on simulation data and real scenes. The reconstruction method of this perspective may bring fresh air to the field of submicron industrial measurement and sophisticated manufacturing.

High-precision 3D reconstruction of multiple magnetic targets based on center weighting method

A high-precision three-dimensional reconstruction method of multiple magnetic targets is studied in this paper. The inversion space is divided into cubic grid cells, and various complex shapes of magnetic targets are simulated by the combination of grid cells. The forward calculation of the magnetic gradient tensor in the spatial domain is performed. Then, the correlation imaging method is used to calculate cross correlation between the observed magnetic gradient tensor and the theoretical magnetic gradient at each grid cell tensor due to a magnetic dipole. The spatial distribution and center position of magnetic targets are preliminarily estimated by the resultant correlation coefficients, and use them to construct the center weighting function as a constraint to the inversion equations. The solution is completed by using the conjugate gradient method to estimate the magnetic susceptibility of each grid cell, and the 3D outline of the magnetic target is depicted by using the visualization technique. The experimental results show that the proposed method can achieve the 3D reconstruction of multiple simple magnetic targets with high precision.

Large-volume FIB-SEM 3D reconstruction: An effective method for characterizing pore space of lacustrine shales

Focused ion beam scanning electron microscopy (FIB-SEM) is a commonly used three-dimensional (3D) pore-network reconstruction method for shales due to its unique capability in imaging nano-scale pores. However, it has been found that for pore space of lacustrine shales with strongly heterogeneous pore structures, the conventional FIB-SEM 3D models usually with dimensions of 10 μm × 10 μm × 10 μm cannot adequately characterize the pore structures as the representative element volume required is much larger than the FIB models. Here, we propose to utilize large volume FIB-SEM (LV-FIB-SEM) 3D models to resolve this challenge. The LV-FIB-SEM model has a significant enhancement in the model size compared with the commonly used conventional FIB-SEM models and a much higher spatial resolution than non-synchrotron nano X-ray CT models for similar imaging sample sizes. With 75 μm × 65 μm × 60 μm as predesigned reconsruction size, after image processing two LV-FIB-SEM 3D models with sizes of 73.56 μm × 38.13 μm × 52.59 μm and 74.01 μm × 43.05 μm × 42.00 μm and model resolution of 30 nm were reconstructed and quantitatively analyzed. When use the conventional FIB-SEM models of 10 μm × 10 μm × 10 μm, the relative deviations between the porosities derived from 100 stochastic models and the average porosity for the two samples studied are −41.13% ∼ +87.31% and −51.66% ∼ +56.05%, respectively, indicating that such small models are not representative of the actual pore structure of the shales investigated. When the model sizes have been increased by 96 times volumetrically, the probabilities of matching average porosities for the two samples increase from 13% to 86% and from 12% to 100%, respectively. This research demonstrates that the upsizing of the FIB-SEM models enables an effective improvement on the representativeness of shale pore structures characterized. It is recommended that LV-FIB-SEM 3D reconstruction be employed to study pore space of lacustrine shales with strongly heterogeneous pore structures, which would enable a more accurate characterization and evaluation of reservoirs for shale oil exploration and development.