Research on 3D reconstruction method of cattle face based on image

The high quality three-dimensional (3D) presentation of computed tomography (CT) data has become a common technique since fast imaging systems and 3D software became widely available at the beginning of the 1980s. Unfortunately, the quality of the 3D images produced by standard 3D packages depends not only on several basic scanning parameters like voltage, amperage, slice, and imcrernent, but also on the primary reconstruction algorithm, image separation, and 3D reconstruction and shading method. Therefore, no standard 3D scanning method providing a high quality 3D display combined with an acceptable acquisition and reconstruction time has been established. New volume scanning methods such as the spiral CT offer additional methods to shorten the examination time. There are some problems for 3D imaging in the use of spiral CT. The aim of our study was to compare different scanning — and 3D — reconstruction methods in order to develop standard scanning sequences for routine 3D CT imaging.

Real-time three-dimensional (3D) reconstruction of real-world environments has many significant applications in various fields, including telepresence technology. When depth sensors, such as those from Microsoft's Kinect series, are introduced simultaneously and become widely available, a new generation of telepresence systems can be developed by combining a real-time 3D reconstruction method with these new technologies. This combination enables users to engage with a remote person while remaining in their local area, as well as control remote devices while viewing their 3D virtual representation. There are numerous applications in which having a telepresence experience could be beneficial, including remote collaboration and entertainment, as well as education, advertising, and rehabilitation. The purpose of this systematic literature review is to analyze the recent advances in 3D reconstruction methods for telepresence systems and the significant related work in this field. Next, we determine the input data and the technological device employed to acquire the input data, which will be utilized in the 3D reconstruction process. The methods of 3D reconstruction implemented in the telepresence system as well as the evaluation of the system, have been extracted and assessed from the included studies. Through the analysis and summarization of many dimensions, we discussed the input data used for the 3D reconstruction method, the real-time 3D reconstruction methods implemented in the telepresence system, and how to evaluate the system. We conclude that real-time 3D reconstruction methods for telepresence systems have progressively improved over the years in conjunction with the advancement of machines and devices such as Red Green Blue-Depth (RGB-D) cameras and Graphics Processing Unit (GPU).

Point cloud enhancement optimization and high-fidelity texture reconstruction methods for air material via fusion of 3D scanning and neural rendering

In view of the existing underwater sensors cannot obtain a relative positional relationship between the target and the underwater vehicle, as well as, target positioning based on binocular vision method which has systematic errors needs more accurate calibration result. An approach for vision of a recognized underwater target is proposed in this paper. The first step is to process the original image with image enhancement and filtering, identify the target image based on HSV threshold. In the next step for target positioning, a three dimensional (3D) re-construction method based on block matching after binocular vision calibration and a 3D re-construction method with learning neural network are studied. Aiming at the problems of slow convergence rate and local optimum of traditional BP networks, several improvements of traditional BP networks are proposed. Finally, the experiment proves that the method can obtain high precision result and good real-time performance under sufficient stylebook data.

Many methods have been proposed for the 3-dimensional (3D) reconstruction of coronaries arteries by combing information from two or more X-ray views of the coronary tree, since the 2D representation of coronary lesion using X-ray coronary angiographies is limited. The aim of this study is to present a new semi-automated method for the accurate 3D reconstruction of coronary arterial bifurcations using X-ray coronary angiographic views (CA). X-ray angiography was acquired from seven patients, both pre and post angioplasty procedure, and their data were used for the 3D reconstruction methodology. The proposed approach consists of 3 steps. Initially, the 2D lumen borders and centerlines are detected. Then the 3D bifurcation path is extracted and the 3D lumen borders are reconstructed around the 3D bifurcation path and finally, the main and side segments are intersected in order to produce the finally model of the bifurcated artery. Considering the X-ray angiography as the gold standard, we validated the proposed method based on the 2D versus the 3D bifurcation segment model. More specifically, in the current dataset our results indicate excellent correlation with the 2D angiography: r = 0.98, p < 0.001; r = 0.97, p < 0.001; r = 0.94, p < 0.001 and r = 0.88, p < 0.001 for the Reference Lumen Diameter (RLD), Minimal Lumen Diameter (MLD), Degree of Stenosis (DS) and Lesion Length (LL), respectively. Moreover the mean values of the Hausdorff Distance and the Dice correlation between the 2D annotated borders and the forwardly projected borders are 0.3031 mm and 91% for the first CA while 0.3103 and 92% for the second one.

ObjectivesThe primary objective was to determine which of the following angular insertion depth (AID) estimation methods showed the strongest correlation with the postoperative AID (AIDpostop) of a lateral wall (LW) electrode: the Escudé formula, based on the largest distance from the round window (RW) to the LW (distance A); the elliptic-circular approximation (ECA) method, based on both distance A and the perpendicular distance (distance B); and a three-dimensional (3D) reconstruction method. The secondary objective was to assess the impact of using the actual electrode insertion length on the evaluation of AID estimation methods.MethodsThis study included 45 cochleae implanted with the Advanced Bionics SlimJ electrode, with all 16 active electrode contacts positioned intracochlear. Distance A, distance B, and a 3D reconstruction along the LW were evaluated on preoperative magnetic resonance imaging using 3D Slicer. The AIDpostop and insertion length were assessed using cone-beam computed tomography. Two sets of estimated AID values were obtained: assuming a perfect 23-mm insertion length with the reference electrode at the RW (AID23mm); and using the actual insertion length (AIDinsertionlength). Spearman’s rank correlation coefficient was used to assess the association between the estimated AID values and AIDpostop.ResultsA moderately positive correlation was observed between AID23mm and AIDpostop for all three approaches, with no statistically significant differences among them (rs=0.410–0.579). When the analysis was repeated using AIDinsertionlength, the strength of the correlation with AIDpostop increased significantly for all three methods, with the 3D reconstruction method showing a stronger correlation (rs=0.952) than the Escudé formula (rs=0.734) or the ECA method (rs=0.787) (P<0.001).ConclusionThe 3D reconstruction approach has the potential to improve prediction of the AID for LW electrode arrays compared to spiral formulae based on 2D cochlear dimensions. Accurate assessment of the actual insertion length is important for the proper evaluation of AID estimation methods.

To address the issues of noise interference,the class imbalance, and insufficient 3D reconstruction accuracy in airborne LiDAR power line point cloud extraction, this paper proposes a method for power line extraction and 3D reconstruction based on an improved PointNet++. Firstly, the dataset is divided, and then multi-dimensional features such as point cloud spatial information and geometric features are extracted through various means. Next, PointNet++ is improved by introducing local relative height features to enhance vertical dimension information. A three-level feature extraction network is constructed, and Focal Loss is adopted in the training stage to solve the class imbalance problem. Dynamic learning rate adjustment and early stopping mechanisms are implemented with power line IoU as the monitoring metric. Finally, the DBSCAN algorithm is used to fit the parametric equation of the power line, and continuous frame point cloud registration is combined to achieve 3D reconstruction of the power corridor. Experimental results show that on a power line point cloud dataset with 4096 points sampled, this method achieves a power line IoU of $\mathbf{9 4 . 9 \%}$ and an overall mIoU of $\mathbf{9 7 . 2 7 \%}$ on the validation set.

3D modelling method and application to a digital campus by fusing point cloud data and image data

In modern medicine, 3D reconstruction plays an important role in improving the accuracy of disease detection and treatment especially in the process of robot-assisted surgery. Accordingly, the choice of 3D reconstruction method is of great importance, which provides real-time visualization. This study analyses and compares two types of methods in 3D medical image reconstruction and visualization: Surface Rendering and Volume Rendering. To be more specific, four algorithms including Contour Filter (CF), Marching Cubes (MC), Composite Volume Rendering (CVR) and Texture Mapping Hardware (TMH) were implemented and compared from the perspective of the rendering effect, speed and interactivity, in which the former two algorithms belong to the concept of Surface Rendering and the latter two algorithms are in the range of Volume Rendering. These algorithms were implemented by Microsoft Visual Studio with Visualization Toolkit (VTK) to reconstruct the 3D images of patients CT image data. The mechanism and realization of these algorithms were described, and the merits and shortcomings of these different rendering methods were compared. Based on the comparison results, the 3D reconstruction and rendering method can be determined according to the specific requirements of robot-assisted surgery.

3D SOC-Net: Deep 3D reconstruction network based on self-organizing clustering mapping

This paper presents a novel approach to both omnidirectional camera calibration and 3D reconstruction of the surrounding scene by contour matching in architectural scenes. By using a quantitative measure to consider the inlier distribution, we can estimate more precise camera model parameters and structure from motion. Since most of line segments of man-made objects are projected to the contours in omnidirectional images, contour matching problem is important in camera recovery process. We propose a novel 3D reconstruction method by contour matching in three omnidirectional views. First, two points on the contour and their viewing vectors are used to determine an interpretation plane equation, and we obtain a contour intersecting both the plane and the estimated patch of the camera model. Then, 3D line segment is calculated from two patches, which is projected to the contour on the third views, and these matching results are used in refinement of camera recovery.

Detecting rail surface anomalies has become crucial for ensuring the safety of train operations. However, manual detection methods are time-consuming and labor-intensive. Although traditional detection models based on one-dimensional signals or two-dimensional images can effectively identify the existence of defects, they are difficult to use to obtain local depth characteristic information, leading to difficulties in accurately assessing the extent of damage in abnormal regions. Rail track maintenance strategies are typically developed based on the detected different depth ranges of defects. To address this issue, this paper proposes a local depth estimation method based on the point cloud of the target rail surface reconstructed by PatchmatchNet. Firstly, according to the acquisition method of multi-view images and the sampling environment at the site, a practical data collection protocol is established for generating a multi-view dataset of rail surface. Next, dense point cloud of the target rail surface were reconstructed by PatchmatchNet. Subsequently, a method for estimating the local anomaly depth is developed based on the point cloud. Experimental results demonstrate that the proposed method achieves a minimum error of approximately 5.5% within a maximum depth range of 0.35mm, when compared to depth measurements obtained using a structured light camera. Finally, this paper presents a more comprehensive three-dimensional representation of the local abnormal physical structure, surpassing traditional one-dimensional and two-dimensional forms. This enhanced representation offers richer information for the effective determination of whether the anomalies constitute defects and aids in distinguishing true defects from other surface irregularities. Such advancement significantly improves the precision of defect assessment and supports the development of highly precise repair strategies.

As core infrastructure of power transmission networks, power towers require high-precision 3D models, which are critical for intelligent inspection and digital twin applications of power transmission lines. Traditional reconstruction methods, such as LiDAR scanning and oblique photogrammetry, suffer from issues including high operational risks, low modeling efficiency, and loss of fine details. To address these limitations, this paper proposes a 3D Gaussian Splatting (3DGS)-based method for power tower 3D reconstruction to enhance reconstruction efficiency and detail preservation capability. First, a multi-view data acquisition scheme combining “unmanned aerial vehicle + oblique photogrammetry” was designed to capture RGB images acquired by Unmanned Aerial Vehicle (UAV) platforms, which are used as the primary input for 3D reconstruction. Second, a sparse point cloud was generated via Structure from Motion. Finally, based on 3DGS, Gaussian model initialization, differentiable rendering, and adaptive density control were performed to produce high-precision 3D models of power towers. Taking two typical power tower types as experimental subjects, comparisons were made with the oblique photogrammetry + ContextCapture method. Experimental results demonstrate that 3DGS not only achieves high model completeness (with the reconstructed model nearly indistinguishable from the original images) but also excels in preserving fine details such as angle steels and cables. Additionally, the final modeling time is reduced by over 70% compared to traditional oblique photogrammetry. 3DGS enables efficient and high-precision reconstruction of power tower 3D models, providing a reliable technical foundation for digital twin applications in power transmission lines. By significantly improving reconstruction efficiency and reducing operational costs, the proposed method supports sustainable power infrastructure inspection, asset lifecycle management, and energy-efficient digital twin applications.

Patients with peripheral artery disease who undergo endovascular treatment are often inflicted by in-stent restenosis. The relation between restenosis and abnormal hemodynamics may be analyzed using patient-specific computational fluid dynamics (CFD) simulations. In this work, first a three-dimensional (3D) reconstruction method, based on an in-house semi-automatic segmentation algorithm of a patient's computed tomography (CT) images with calcification and metallic artifacts, and thrombus removal is described. The reconstruction method was validated using 3D printed rigid phantoms of stented femoral arteries by comparing the reconstructed geometries with the reference computer-aided design (CAD) geometries employed for 3D printing. The mean reconstruction error resulting from the validation of the reconstruction method was ~6% in both stented and non-stented regions. Secondly, a patient-specific model of the stented femoral artery was created and CFD analyses were performed with emphasis on the selection of the boundary conditions. CFD results were compared in scenarios with and without common femoral artery bifurcation, employing flat or parabolic inlet velocity profiles. Similar helical flow structures were visible in all scenarios. Negligible differences in wall shear stress (<0.5%) were found in the stented region. In conclusion, a robust method, applicable to patient-specific cases of stented diseased femoral arteries, was developed and validated.

Accurate and efficient detection of road damage is crucial in traffic safety and maintenance management. Traditional road detection methods have problems such as low efficiency and insufficient accuracy, making it difficult to meet the needs of large-scale road health assessments. With the development of drone technology and computer vision, new ideas have been provided for the automatic detection of road diseases. The existing drone-based road detection methods have poor performance in dealing with complex road scenes such as vehicle occlusion, and there is still room for improvement in 3D modeling accuracy and disease detection accuracy, lacking a comprehensive and efficient solution. This paper proposes a UAV (Unmanned Aerial Vehicle)-based 3D reconstruction and large-scale disease detection method for roads. By capturing aerial images with UAVs and utilizing an improved YOLOv8 model, vehicles in the images are identified and removed. Apply MVSNet (Multi-View Stereo Network) 3D reconstruction algorithm for road surface modeling, and finally use point cloud processing and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering for disease detection. The experimental results show that this method performs excellently in terms of 3D modeling accuracy and speed. Compared with the traditional colmap method, the reconstruction speed is greatly improved, and the reconstruction density is three times that of colmap. Meanwhile, the reconstructed point cloud can effectively detect road smoothness and settlement. This study provides a new method for effective disease detection under complex road conditions, suitable for large-scale road health assessment tasks.