3D Reconstruction Method Research Articles

An experimental investigation of the erosion process and morphological characteristics of the tile-back type slope with non-uniform slopes based on a 3D reconstruction method

Tile-back type slopes comprise ephemeral gullies (EGs) and hillslopes; they are a unique and widely distributed micro-landform in the Loess Plateau region of China. Gully erosion from these landforms is a serious issue, but the micro-landform makes the erosion process and its estimation complex. Quantifying soil erosion processes and their distribution characteristics at different positions on tile-back type slopes will provide a clearer picture for ecological restoration to control further soil degradation. This study investigated the erosion process of tile-back type slope with non-uniform slopes using a 3D photo-reconstruction method during eight successive simulated rainfall events. The results showed that EG erosion began with a chain of intermittent headcuts. When the accumulated rainfall reached 76 mm, serious collapses dramatically increased the amount of sediment by 216% after the first rainfall (cumulative rainfall was about 15 mm). We quantified the sediment contribution of EG erosion (46.20%), rill erosion (35.62%), and inter-rill erosion (18.18%) to total soil loss. The erosion area of the steep slope section and extremely steep slope section accounted for 33.26% and 66.74% of the total erosion area, respectively. Moreover, sediment amounts significantly correlated with morphological parameters, particularly the amount of EG erosion and maximum gully depth, with a correlation coefficient of 0.98. Cumulative gully length and erosion area had the greatest effect on rill erosion, with a correlation coefficient of 0.97. These results provide insight into the qualitative and quantitative understanding of EG erosion process on Loess Plateau of China and an important reference for the rational arrangement of EG control measures.

Pavement crack measurement based on aerial 3D reconstruction and learning-based segmentation method

Rapid inspection of urban road cracks is vital to maintain traffic smoothness and ensure traffic safety. A rapid pavement crack inspection method uses low-altitude aerial images captured by an unmanned aerial system (UAS) and deep-learning aided 3D reconstruction, and a learning-based object segmentation algorithm is proposed to measure road cracks automatically. The contributions include: (a) An efficient 3D reconstruction method for low-altitude aerial images captured by a UAS is proposed, which applies an instance segmentation network to segment road targets from raw images with complex backgrounds first and then performs structure from motion to reconstruct a large-scale road orthophoto from a large number of aerial images. (b) To detect cracks from the reconstructed large-size road orthophoto, a sliding window algorithm and U-Net model optimized with a transformer structure are used to automatically identify and segment the cracks from the orthophoto at the pixel level. Then, a connected domain feature analysis method is used to measure the road crack length. The proposed method is applied to detection of road cracks in a 1.5 km2 area of a city. The results show that the proposed method can effectively and accurately detect cracks and measure the length of cracks in the 4-km-long road, which proves the practicality of the proposed method.

Quantifying the Relationship Between Microstructure and Performance in Gadolinium-Doped Ceria Infiltrated Ni/YSZ Symmetric Cells

Infiltration of mixed ionic/electronic conducting nanocatalysts into the anodes of solid oxide fuel cells allows the hydrogen oxidation reaction to occur at double-phase boundaries (DPBs) rather than being confined only to the triple-phase boundaries (TPBs). In this research, we use a combination of cross-sectional scanning electron microscopy, three-dimensional (3D) reconstruction, and electrochemical methods to characterize both baseline and gadolinium-doped ceria (GDC)-infiltrated symmetric cells, with the goal of establishing a comparison between the density of electrochemically active sites in uninfiltrated and GDC-infiltrated cells. Electrochemical impedance spectroscopy and distribution of relaxation times analysis showed GDC infiltration decreased the overall polarization of the anode by a factor of 8.78× and decreased the resistance associated with the reactions at the TPBs/DPBs by a factor of over 21×. By quantifying expected GDC deposition morphology in the Ni/YSZ anode, it is estimated that reactions occur in the baseline cell up to 84 nm from the active TPB lines.

Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model

Three-dimensional (3D) reconstruction techniques are playing an increasingly important role in education and entertainment. Real and recognizable avatars can enhance the immersion and interactivity of virtual systems. In 3D face modeling technology, face texture carries vital face recognition information. Therefore, this study proposes a panoramic 3D face texture generation method for 3D face reconstruction from a single 2D face image based on a 3D Morphable model (3DMM). Realistic and comprehensive panoramic facial textures can be obtained using generative networks as texture converters. Furthermore, we propose a low-cost method for generating face texture datasets for data collection. Experimental results show that the proposed method can generate panoramic face textures for 3D face meshes from a single image input, resulting in the final generation of textured 3D models that look realistic from different viewpoints.

Alterations in microglial morphology concentrate in the habitual sleeping period of the mouse.

In nocturnal animals, waking appears during the dark period while maximal non-rapid-eye-movement sleep (NREMS) with electroencephalographic slow-wave-activity (SWA) takes place at the beginning of the light period. Vigilance states associate with variable levels of neuronal activity: waking with high-frequency activity patterns while during NREMS, SWA influences neuronal activity in many brain areas. On a glial level, sleep deprivation modifies microglial morphology, but only few studies have investigated microglia through the physiological sleep-wake cycle. To quantify microglial morphology (territory, volume, ramification) throughout the 24 h light-dark cycle, we collected brain samples from inbred C57BL male mice (n=51) every 3h and applied a 3D-reconstruction method for microglial cells on the acquired confocal microscopy images. As microglia express regional heterogeneity and are influenced by local neuronal activity, we chose to investigate three interconnected and functionally well-characterized brain areas: the somatosensory cortex (SC), the dorsal hippocampus (HC), and the basal forebrain (BF). To temporally associate microglial morphology with vigilance stages, we performed a 24 h polysomnography in a separate group of animals (n=6). In line with previous findings, microglia displayed de-ramification in the 12 h light- and hyper-ramification in the 12 h dark period. Notably, we found that the decrease in microglial features was most prominent within the early hours of the light period, co-occurring with maximal sleep SWA. By the end of the light period, all features reached maximum levels and remained steadily elevated throughout the dark period with minor regional differences. We propose that vigilance-stage specific neuronal activity, and SWA, could modify microglial morphology.

3D velocity field reconstruction of gas-liquid two-phase flow based on space-time multi-scale binocular-PIV technology.

for particle image velocimetry (PIV) technique, the two-dimensional (2D) PIV by one camera can only obtain 2D velocity field, while three-dimensional (3D) PIV based on tomography by three or four cameras is always complex and expensive. In this work, a binocular-PIV technology based on two cameras was proposed to reconstruct the 3D velocity field of gas-liquid two-phase flow, which is a combination of the binocular stereo vision and cross-correlation based on fast Fourier transform (CC-FFT). The depth of particle was calculated by binocular stereo vision on space scale, and the plane displacement of particles was acquired by CC-FFT on time scale. Experimental results have proved the effectiveness of the proposed method in 3D reconstruction of velocity field for gas-liquid two-phase flow.

Implicit Neural Deformation for Sparse‐View Face Reconstruction

AbstractIn this work, we present a new method for 3D face reconstruction from sparse‐view RGB images. Unlike previous methods which are built upon 3D morphable models (3DMMs) with limited details, we leverage an implicit representation to encode rich geometric features. Our overall pipeline consists of two major components, including a geometry network, which learns a deformable neural signed distance function (SDF) as the 3D face representation, and a rendering network, which learns to render on‐surface points of the neural SDF to match the input images via self‐supervised optimization. To handle in‐the‐wild sparse‐view input of the same target with different expressions at test time, we propose residual latent code to effectively expand the shape space of the learned implicit face representation as well as a novel view‐switch loss to enforce consistency among different views. Our experimental results on several benchmark datasets demonstrate that our approach outperforms alternative baselines and achieves superior face reconstruction results compared to state‐of‐the‐art methods.

Morphological classification and three-dimensional pore structure reconstruction of shale oil reservoirs: A case from the second member of Paleogene Kongdian Formation in the Cangdong Sag, Bohai Bay Basin, East China

This study combines large volume three-dimensional reconstruction via focused ion beam scanning electron microscopy (FIB-SEM) with conventional scanning electron microscope (SEM) observation, automatic mineral identification and characterization system (AMICS) and large-area splicing of SEM images to characterize and classify the microscopic storage space distribution patterns and 3D pore structures of shales in the second member of the Paleogene Kongdian Formation (Kong 2) in the Cangdong Sag of the Bohai Bay Basin. It is shown that: (1) The Kong 2 Member can be divided into seven types according to the distribution patterns of reservoir spaces: felsic shale with intergranular micron pores, felsic shale with intergranular fissures, felsic shale with intergranular pores, hybrid shale with intergranular pores and fissures, hybrid shale with intergranular pores, clay-bearing dolomitic shale with intergranular pores, and clay-free dolomitic shale with intergranular pores. (2) The reservoir of the intergranular fracture type has better storage capacity than that of intergranular pore type. For reservoirs with storage space of intergranular pore type, the dolomitic shale reservoir has the best storage capacity, the hybrid shale comes second, followed by the felsic shale. (3) The felsic shale with intergranular fissures has the best storage capacity and percolation structure, making it the first target in shale oil exploration. (4) The large volume FIB-SEM 3D reconstruction method is able to characterize a large shale volume while maintaining relatively high spatial resolution, and has been demonstrated an effective method in characterizing the 3D storage space in strongly heterogeneous continental shales.

Sigma-FP: Robot Mapping of 3D Floor Plans With an RGB-D Camera Under Uncertainty

This letter presents Sigma-FP, a novel 3D reconstruction method to obtain the floor plan of a multi-room environment from a sequence of RGB-D images captured by a wheeled mobile robot. For each input image, the planar patches of visible walls are extracted and subsequently characterized by a multivariate Gaussian distribution in the convenient Plane Parameter Space. Then, accounting for the probabilistic nature of the robot localization, we transform and combine the planar patches from the camera frame into a 3D global model, where the planar patches include both the plane estimation uncertainty and the propagation of the robot pose uncertainty. Additionally, processing depth data, we detect openings (doors and windows) in the wall, which are also incorporated in the 3D global model to provide a more realistic representation. Experimental results, in both real-world and synthetic environments, demonstrate that our method outperforms state-of-the-art methods, both in time and accuracy, while just relying on Atlanta world assumption.

Power Pylon Reconstruction from Airborne LiDAR Data Based on Component Segmentation and Model Matching

In recent years, with the rapid growth of State Grid digitization, it has become necessary to perform three-dimensional (3D) reconstruction of power elements with high efficiency and precision to achieve full coverage when simulating important transmission lines. Limited by the performance of acquisition equipment and the environment, the actual scanned point cloud usually has problems such as noise interference and data loss, presenting a great challenge for 3D reconstruction. This study proposes a model-driven 3D reconstruction method based on Airborne LiDAR point cloud data. Firstly, power pylon redirection is realized based on the Principal Component Analysis (PCA) algorithm. Secondly, the vertical and horizontal distribution characteristics of the power pylon point cloud and the graphical characteristics of the overall two-dimensional (2D) orthographic projection are analyzed to determine segmentation positions and the key segmentation position of the power pylon. The 2D alpha shape algorithm is adopted to obtain the pylon body contour points, and then the pylon feature points are extracted and corrected. Based on feature points, the components of original pylon and model pylon are registered, and the distance between the original point cloud and the model point cloud is calculated at the same time. Finally, the model with the highest matching degree is regarded as the reconstructed model of the pylon. The main advantages of the proposed method include: (1) identifying the key segmentation position according to the graphical characteristics; (2) for some pylons with much missing data, the complete model can be accurately reconstructed. The average RMSE (Root-Mean-Square Error) of all power pylon components in this study was 15.4 cm. The experimental results reveal that the effects of power pylon structure segmentation and reconstruction are satisfactory, which provides method and model support for digital management and security analysis of transmission lines.

Review of key technologies for warehouse 3D reconstruction

Most of the current warehouse management system is made out of two-dimensional (2D) plane schematic warehouse, which brings a lot of inconvenience to warehouse management, including the warehouse data, storage of goods, location search, inventory, etc. 3D warehouse model began to attract attention as it can provide more intuitive view of warehouse-related information. This paper aims to review and investigate the current key technologies used for 3D modeling of warehouse system. This paper reviewed the method of 3D view reconstruction of the warehouse management system, including the active and passive, active and passive fusion methods, and makes a detailed comparison between the active and passive methods. It was found that different methods were applied to reconstruct the 3D view of warehouse, each with its own advantages and disadvantages.

Enhancing 3D Reconstruction Model by Deep Learning and Its Application in Building Damage Assessment after Earthquake

A timely and accurate damage assessment of buildings after an earthquake is critical for the safety of people and property. Most of the existing methods based on classification and segmentation use two-dimensional information to determine the damage level of the buildings, which cannot provide the multi-view information of the damaged building, resulting in inaccurate assessment results. According to the knowledge of the authors, there is no related research using the deep-learning-based 3D reconstruction method for the evaluation of building damage. In this paper, we first applied the deep-learning-based MVS model to reconstruct the 3D model of the buildings after an earthquake using multi-view UAV images, to assist the building damage assessment task. The method contains three main steps. Firstly, the camera parameters are calculated. Then, 3D reconstruction is conducted based on CasMVSNet. Finally, a building damage assessment is performed based on the 3D reconstruction result. To evaluate the effectiveness of the proposed method, the method was tested in multi-view UAV aerial images of Yangbi County, Yunnan Province. The results indicate that: (1) the time efficiency of CasMVSNet is significantly higher than that of other deep learning models, which can meet the timeliness requirement of post-earthquake rescue and damage assessment. In addition, the memory consumption of CasMVSNet is the lowest; (2) CasMVSNet exhibits the best 3D reconstruction result in both high and small buildings; (3) the proposed method can provide detail and multi-view information of damaged buildings, which can be used to assist the building damage assessment task. The results of the building damage assessment are very similar to the results of the field survey.

Semantics-and-Primitives-Guided Indoor 3D Reconstruction from Point Clouds

The automatic 3D reconstruction of indoor scenes is of great significance in the application of 3D-scene understanding. The existing methods have poor resilience to the incomplete and noisy point cloud, which leads to low-quality results and tedious post-processing. Therefore, the objective of this work is to automatically reconstruct indoor scenes from an incomplete and noisy point-cloud base on semantics and primitives. In this paper, we propose a semantics-and-primitives-guided indoor 3D reconstruction method. Firstly, a local, fully connected graph neural network is designed for semantic segmentation. Secondly, based on the enumerable features of indoor scenes, a primitive-based reconstruction method is proposed, which retrieves the most similar model in a 3D-ESF indoor model library by using ESF descriptors and semantic labels. Finally, a coarse-to-fine registration method is proposed to register the model into the scene. The results indicate that our method can achieve high-quality results while remaining better resilience to the incompleteness and noise of point cloud. It is concluded that the proposed method is practical and is able to automatically reconstruct the indoor scene from the point cloud with incompleteness and noise.

Fetal 3D cardiovascular cine image acquisition using radialsampling and compressed sensing.

To explore a fetal 3D cardiovascular cine acquisition using a radial image acquisition and compressed-sensing reconstruction and compare image quality and scan time with conventional multislice 2D imaging. Volumetric fetal cardiac data were acquired in 26 volunteers using a radial 3D balanced SSFP pulse sequence. Cardiac gating was performed using a Doppler ultrasound device. Images were reconstructed using a parallel-imaging and compressed-sensing algorithm. Multiplanar reformatting to standard cardiac views was performed before image analysis. Clinical 2D images were used for comparison. Qualitative and quantitative image evaluation were performed by two experienced observers (scale: 1-4). Volumes, mass, and function were assessed. Average scan time for the 3D imaging was 6min, including one localizer. A 2D imaging stack covering the entire heart including localizer sequences took at least 6.5min, depending on planning complexity. The 3D acquisition was successful in 7 of 26 subjects (27%). Overall image contrast and perceived resolution were lower in the 3D images. Nonetheless, the 3D images had, on average, a moderate cardiac diagnostic quality (median [range]: 3 [1-4]). Standard clinical 2D acquisitions had a high cardiac diagnostic quality (median [range]: 4 [3, 4]). Cardiac measurements were not different between 2D and 3D images (all p> 0.16). The presented free-breathing whole-heart fetal 3D radial cine MRI acquisition and reconstruction method enables retrospective visualization of all cardiac views while keeping examination times short. This proof-of-concept work produced images with diagnostic quality, while at the same time reducing the planning complexity to a single localizer.

Texture-less surface reconstruction using shape-based image augmentation

The development of intelligent Robot-Assisted Minimally Invasive Surgery demands geometric reconstruction from endoscopic images. However, images of human tissue surfaces are commonly texture-less. Obtaining the dense depth map of a texture-less scene is very difficult because traditional feature-based 3D reconstruction methods cannot detect enough features to build dense correspondences for depth computation. Given this problem, this study proposes a novel reconstruction method based on our shape-based image augmentation method. The main contribution of this method is the provision of a novel means to resolve the texture-less problem mainly on the input data level. In our method, we first calculate two shape gradient maps using Shape-From-Shading (SFS) method and we build Fast Point Feature Histogram (FPFH) 3D descriptor map according to the shape. Second, a series of augmented images can be computed by combining shape gradient maps, FPFH map, and the original image with different weights. Finally, we detect features on the new augmented images. Based on feature calculated sparse depth information and SFS calculated dense shape information, we further integrate a rectified dense depth map. Experiments show that our method can reconstruct texture-less surfaces with good accuracy.

Development and Validation of a Mobile Application for Measuring Tibial Torsion.

Tibial torsion lacks a single and reliable method for its measurement. While physical examination, computed tomography (CT), and EOS imaging are used complementarily, three-dimensional (3D) CT is the most widely used method for intuitive documentation and visualization. However, concern regarding the associated radiation hazard limits its use in the evaluation of pediatric patients. Moreover, EOS machines are too expensive and too large to be placed in every clinic requiring the measurement of tibial torsion. Therefore, a new method for 3D reconstruction is needed. In the present study, we tested the validity and reliability of a novel reconstruction tool for the lower leg. A statistical shape model and Laplacian constraint were adopted for the development of a new reconstruction tool for measuring tibial torsion. Tibial torsion measurements based on a 3D reconstruction application and CT images for 36 patients were evaluated for intraobserver and interobserver reliability. Tibial torsion measurements for 75 patients were compared for validation. A 3D reconstruction system for the lower leg was developed as a mobile application and was installed in a portable device for easy access in the clinical setting. In terms of interobserver reliability, the intraclass correlation coefficient among 3 clinicians was 0.896 (95% confidence interval [CI], 0.828 to 0.941). The correlation coefficient between tibial torsion measured with use of 3D CT and that measured with the mobile application was 0.865 (p < 0.001). The mobile application showed excellent reliability and validity for measuring tibial torsion. Concurrent utilization with mobile application for the femur allows visualization of the rotational profile of the leg without the need for CT or EOS. Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence.

3D reconstruction of non-structural surface of a stiffened hull plate based on RGB-D images

Deformation measurement of hull structure is of great significance for enhancing the quality of shipbuilding. In this paper, a three-dimensional (3D) reconstruction method based on RGB-D images is presented for non-structural surface of a stiffened hull plate. The algorithm for object surface region nodes (OSRN) is proposed to extract the features of point clouds and generate higher-order fitting surface model. Firstly, random sample consensus (RANSAC) algorithm and least square method are applied to eliminate the noise of point clouds. Spatial position of targets in RGB image and singular value decomposition (SVD) are utilized to accomplish the registration of point clouds from multiple camera views. Then, the point clouds data are clustered and fused to fit the quadric surface. On this basis, the 3D surface model is reconstructed through higher-order panel element. Two comparative experiments, including the measurements via the RGB-D camera and 3D Laser Scanner, are carried out to verify the applicability and accuracy of the proposed method.

Three-dimensional measurement of periodontal support during surgical orthodontic treatment of high-angle skeletal Class III malocclusion: A retrospective study

This study aimed to quantify the periodontal health of incisors during surgical orthodontic treatment in patients with high-angle Class III malocclusion using a cone-beam computed tomography (CBCT) 3-dimensional (3D) reconstruction technique. The sample consisted of 30 patients with high-angle Class III malocclusion (mean age, 20.53 ± 2.86 years). CBCT images were taken before treatment (T0), after presurgical orthodontic treatment, and after treatment (T2). In addition, 3D tooth and alveolar bone models were generated. The root surface area, periodontal ligament (PDL)_Area, and vertical bone level (VBL) around the maxillary and mandibular central incisors were measured. The root surface area and PDL_Area of maxillary and mandibular central incisors decreased continuously between T0 and T2 (P<0.01). At T2, mandibular central incisors showed 38.64 ± 13.39% PDL_Area loss, and maxillary central incisors exhibited 21.13 ± 16.48% PDL_Area loss. For mandibular central incisors, the PDL_Area loss caused by VBL loss was significantly greater than that for maxillary central incisors (P<0.01) and significantly greater than the PDL_Area loss caused by root resorption (P<0.01). From T0 to T2, the lingual surface of maxillary central incisors exhibited greater VBL loss than the other 3 surfaces (P<0.01), and the labial and lingual surfaces ofmandibular central incisors demonstrated greater VBL loss than proximal surfaces (P<0.01). The 3D CBCT reconstruction method provides useful information regarding the periodontal defects of incisors in patients with high-angle skeletal Class III malocclusion. The PDL_Area of maxillary and mandibular central incisors decreased continuously during the treatment. Vertical alveolar bone levels at proximal surfaces appeared to be relatively stable.

Three-dimensional reconstruction from a fringe projection system through a planar transparent medium.

A vision measurement system is placed in a protective cover made of a transparent medium to avoid environmental influences. Due to the deflection of light rays on the front and rear surfaces of the transparent medium, the imaging position of an object on the camera target plane is deviated, which makes the traditional vision detection methods based on the triangulation principle produce large measurement errors. This work introduces a three-dimensional (3D) reconstruction method by fringe projection system through a planar transparent medium. We derive the coordinate transformation relationship between a real-object point and a pseudo-object point caused by light refraction based on Snell's law of flat refraction. Based on the relationship, a modified fringe projection method is proposed for unbiased 3D reconstruction. Two experiments, including 3D shape measurement of a white plate with ring markers and 3D shape measurement of a regular spherical object are conducted. The results demonstrate the effectiveness of the proposed method in such measurement environment.

3D Reconstruction Method of Space Target on Optical Images with Wide Baseline via Neural Radiance Field

3D reconstruction of space targets using optical measurement data is an important topic in the field of space surveillance and spacecraft service in orbit. The core of current mainstream 3D reconstruction methods is to establish feature association between optical images. In space-based optical imaging scenes, the texture of target region is missing and the range of view is large, which causes great difficulties in feature extraction and matching of image sequences. Neural radiance field technology does not need to extract explicit features, and its implicit expression of 3D scenes has great potential for solving reconstruction problems in weak texture and broad baseline cases. Therefore, a novel 3D reconstruction method for a sequence of images based on neural radiance field is proposed. The proposed method breaks through the limitation of traditional 3D reconstruction methods which rely on rich texture and narrow baseline image sequences. Simulation experiments show that compared with the existing methods for space target images with weak texture and wide baseline, our method performs better in terms of accuracy and completeness.