Image processing based three-dimensional model reconstruction for cross-platform numerical simulation

Numerical simulation is the most powerful computational and analysis tool for a large variety of engineering and physical problems.For a complex problem relating to multi-field,multi-process and multi-scale,different computing tools have to be developed so as to solve particular fields at different scales and for different processes.Therefore,the integration of different types of software is inevitable.However,it is difficult to perform the transfer of the meshes and simulated results among software packages because of the lack of shared data formats or encrypted data formats.An image processing based method for three-dimensional model reconstruction for numerical simulation was proposed,which presents a solution to the integration problem by a series of slice or projection images obtained by the post-processing modules of the numerical simulation software.By means of mapping image pixels to meshes of either finite difference or finite element models,the geometry contour can be extracted to export the stereolithography model.The values of results,represented by color,can be deduced and assigned to the meshes.All the models with data can be directly or indirectly integrated into other software as a continued or new numerical simulation.The three-dimensional reconstruction method has been validated in numerical simulation of castings and case studies were provided in this study.

Cosmetic Safety Evaluation Based on In Vitro Three-dimensional Reconstructed Human Epidermis(3D-RHE) Models

Cosmetic safety evaluation employs a series of toxicological tests, on both qualitative and quantitative levels, to assess the potential risks for the daily use of selected cosmetic ingredients and final products. Traditionally, safety evaluation of cosmetics uses animal tests. With the development of in vitro science and the 3R （Reduction, Replacement and Refinement） principle, three-dimensional reconstructed human epidermis （3D-RHE） models have been developed and widely applied in cosmetic safety evaluation. Reconstructed human skin models possess anatomy and metabolism biology similar to real human tissue. This paper reviews the current application of 3D-RHE models in the safety evaluation of skin irritation, eye irritation, phototoxicity and genotoxicity potential of cosmetic ingredients/formulas. The advantages and disadvantages of using skin models are also discussed, and comments and suggestions are given for its future development.

MODEL RECONSTRUCTION FROM CLOUD DATA FOR RAPID PROTOTYPE MANUFACTURING

Model reconstruction from points scanned on existing physical objects is much important in a variety of situations such as reverse engineering for mechanical products, computer vision and recovery of biological shapes from two dimensional contours. With the development of measuring equipment, cloud points that contain more details of the object can be obtained conveniently. On the other hand, large quantity of sampled points brings difficulties to model reconstruction method. This paper first presents an algorithm to automatically reduce the number of cloud points under given tolerance. Triangle mesh surface from the simplified data set is reconstructed by the marching cubes algorithm. For various reasons, reconstructed mesh usually contains unwanted holes. An approach to create new triangles is proposed with optimized shape for covering the unexpected holes in triangle meshes. After hole filling, watertight triangle mesh can be directly output in STL format, which is widely used in rapid prototype manufacturing. Practical examples are included to demonstrate the method.

Three-Dimensional Model Reconstruction of Nonwovens from Multi-Focus Images

The three-dimensional(3D)model is of great significance to analyze the performance of nonwovens.However,the existing modelling methods could not reconstruct the 3D structure of nonwovens at low cost.A new method based on deep learning was proposed to reconstruct 3D models of nonwovens from multi-focus images.A convolutional neural network was trained to extract clear fibers from sequence images.Image processing algorithms were used to obtain the radius,the central axis,and depth information of fibers from the extraction results.Based on this information,3D models were built in 3D space.Furthermore,self-developed algorithms optimized the central axis and depth of fibers,which made fibers more realistic and continuous.The method with lower cost could reconstruct 3D models of nonwovens conveniently.

3D Model Reconstruction Based on Process Information

The traditional strategy of 3D model reconstruction mainly concentrates on orthographic projections or engineering drawings. But there are some shortcomings. Such as, only few kinds of solids can be reconstructed, the high complexity of time and less information about the 3D model. The research is extended and process card is treated as part of the 3D reconstruction. A set of process data is a superset of 2D engineering drawings set. The set comprises process drawings and process steps, and shows a sequencing and asymptotic course that a part is made from roughcast blank to final product. According to these characteristics, the object to be reconstructed is translated from the complicated engineering drawings into a series of much simpler process drawings. With the plentiful process information added for reconstruction, the disturbances such as irrelevant graph, symbol and label, etc. can be avoided. And more, the form change of both neighbor process drawings is so little that the engineering drawings interpretation has no difficulty; in addition, the abnormal solution and multi-solution can be avoided during reconstruction, and the problems of being applicable to more objects is solved ultimately. Therefore, the utility method for 3D reconstruction model will be possible. On the other hand, the feature information in process cards is provided for reconstruction model. Focusing on process cards, the feasibility and requirements of Working Procedure Model reconstruction is analyzed, and the method to apply and implement the Natural Language Understanding into the 3D reconstruction is studied. The method of asymptotic approximation product was proposed, by which a 3D process model can be constructed automatically and intelligently. The process model not only includes the information about parts characters, but also can deliver the information of design, process and engineering to the downstream applications.

Reconstruction of symmetric models composed of analytic curves and surfaces from point cloud

This paper presents a method to reconstruct symmetric geometric models from point cloud with inherent symmetric structure. Symmetry types commonly found in engineering parts, i.e., translational, reflectional and rotational symmetries are considered. The reconstruction problem is formulated as a constrained optimization, where the objective function is the sum of squared distances of points to the model, and constraints are enforced to keep geometric relationships in the model. First, the explicit representations of symmetric models are presented. Then, by using the concept of parameterized points (where the coor-dinate components are represented as functions rather than constants), the distances of points to symmetric models are deduced. With these distance functions, symmetry information, for both 2D and 3D models, is uniformly represented in the process of reconstruction. The constrained optimization problem is solved by a standard nonlinear optimization method. Owing to the explicit representation of symmetry information, the computational complexity of our method is reduced greatly. Finally, examples are given to demonstrate the application of the proposed method.

Curve interpolation model for visualising disjointed neural elements

Neuron cell are built from a myriad of axon and denddte structures. It transmits electrochemical signals between the brain and the nervous system. Three-dimensional visualization of neuron structure could help to facilitate deeper understanding of neuron and its models. An accurate neuron model could aid understanding of brain＇s functionalities, diagnosis and knowledge of entire nervous system. Existing neuron models have been found to be defective in the aspect of realism. Whereas in the actual biological neuron, there is continuous growth as the soma extending to the axon and the dendrite; but, the current neuron visualization models present it as disjointed segments that has greatly mediated effective realism. In this research, a new reconstruction model comprising of the Bounding Cylinder, Curve Interpolation and Gouraud Shading is proposed to visualize neuron model in order to improve realism. The reconstructed model is used to design algorithms for generating neuron branching from neuron SWC data. The Bounding Cylinder and Curve Interpolation methods are used to improve the connected segments of the neuron model using a series of cascaded cylinders along the neuron＇s connection path. Three control points are proposed between two adjacent neuron segments. Finally, the model is rendered with Gouraud Shading for smoothening of the model surface. This produce a near-perfection model of the natural neurons with attended realism. The model is validated by a group of bioinformatics analysts＇ responses to a predefined survey. The result shows about 82% acceptance and satisfaction rate.

Geometric Feature Extraction and Model Reconstruction Based on Scattered Data

A method of 3D model reconstruction based on scattered point data in reverse engineering is presented here. The topological relationship of scattered points was established firstly, then the data set was triangulated to reconstruct the mesh surface model. The curvatures of cloud data were calculated based on the mesh surface, and the point data were segmented by edge-based method; Every patch of data was fitted by quadric surface of freeform surface, and the type of quadric surface was decided by parameters automatically, at last the whole CAD model was created. An example of mouse model was employed to confirm the effect of the algorithm.

Preoperative 3D reconstruction and fluorescent indocyanine green for laparoscopic duodenum preserving pancreatic head resection:A case report

BACKGROUND Duodenum-preserving pancreatic head resection(DPPHR)is the choice of surgery for benign or low-grade malignant tumors of the pancreatic head.Laparoscopic DPPHR(LDPPHR)procedure can be improved by preoperative 3D model reconstruction and the use of intravenous indocyanine green fluorescent before surgery for real-time navigation with fluorescent display to guide the surgical dissection and prevention of from injury to vessels and biliary tract.CASE SUMMARY Here we report the successful short-and long-term outcomes after one year following LDPPHR for a 60-year lady who had an uneventful recovery and was discharged home one week after the surgery.CONCLUSION There was no bile leakage or pancreatic leakage or delayed gastric emptying.The histopathology report showed multiple cysts in the pancreatic head and localized pancreatic intraepithelial tumor lesions.The resected margin was free of tumor.

Satellite anomaly detection based on reconstruction discrepancy theory utilizing a new dual-branch reconstruction model

To enhance the accuracy of deep learning methods based on reconstruction discrepancy in satellite anomaly detection tasks,this study proposes a dual-branch reconstruction model(DBRM)and designs a comprehensive satellite anomaly detection framework around this model.Firstly,we introduce the temporal-channel mixer(TC-Mixer)module,which mainly comprises a self-attention layer for capturing long-range temporal dependencies in telemetry data,and two types of feed-forward networks(FFN)for extract-ing complex patterns in the temporal and channel dimension of telemetry data.This design endows the TC-Mixer module with robust capabilities for extracting complicated dependencies in telemetry data.Secondly,with the TC-Mixer module as the main component,we designed the DBRM.This model utilizes a shared latent representation layer,allowing the regeneration branch and forecasting branch of the DBRM to share most of the feature extraction network architecture.This approach significantly en-hances the model’s regression accuracy while reducing computational complexity.Thirdly,using the DBRM as the core network model,we devised a comprehensive satellite anomaly detection framework.This includes an anomaly criterion that considers the reconstruction discrepancy of both the regeneration and forecasting branches,the peak-over-threshold(POT)method for anomaly thresholding,and the MIC-based feature engineering method,etc.Finally,we conducted comparative experiments with several SOTA anomaly detection algorithms on two public and one private satellite anomaly detection datasets.The experimental results validate the effectiveness and superiority of our proposed method.

A Preliminary Study on the Intensity of Cold Wave Storm Surges of Laizhou Bay

Abstract Dike failure and marine losses are quite prominent in Laizhou Bay during the period of cold wave storm surges because of its open coastline to the north and fiat topography. In order to evaluate the intensity of c01d wave storm surge, the hindcast of ma- rine elements induced by cold waves in Laizhou Bay from 1985 to 2004 is conducted using a cold wave storm surge-wave coupled model and the joint return period of extreme water level, concomitant wave height, and concomitant wind speed are calculated. A new criterion of cold wave storm surge intensity based on such studies is developed. Considering the frequency of cold wave, this paper introduces a Poisson trivariate compound reconstruction model to calculate the joint return period, which is closer to the reality. By using the newly defined cold wave storm surge intensity, the ＇cold wave grade＇ in meteorology can better describe the severity of cold wave storm surges and the warning level is well corresponding to different intensities of cold wave storm surges. Therefore, it provides a proper guidance to marine hydrological analysis, disaster prevention and marine structure design in Laizhou Bay.

Dynamics of moisture regime and its reconstruction from a tree-ring width chronology of Pinus sylvestris in the downstream basin of the Selenga River, Russia

Regional tree-ring width chronology of the Scots pine (Pinus sylvestris L.) was constructed from 8 sites in the forest-steppe belt situated in the foothills of the Selenga River basin, Russia. Moisture information contained in tree-ring width chronology was obtained through linear regression reconstruction models of annual August–July precipitation and annual water discharge of the Selenga River during the period 1767–2015. Comparison of the smoothed series allowed estimating long-term variation component of these moisture regime parameters with a high precision. At the same time, regional drought indices are less correlated with pine radial growth, because they have contribution of the other environmental variables, which are much less reflected in the tree-ring of the investigated pine forest stands. Reconstructed dynamic of the moisture regime parameters is supported by documental evident of many socially significant events in the regional history, such as crop failures caused by both droughts and floods, and catastrophic fire in the Irkutsk City in 1879. Also, dependence of the amount of precipitation in the study area on the atmospheric circulation in Central Asia is revealed to have a similar pattern with other regions, i.e., a negative relationship of precipitation with the development of large high atmospheric pressure area within its center in the Altai and Tianshan mountains.

Implicit Shape Reconstruction of Unorganized Points Using PDE-Based Deformable 3D Manifolds

In this work we consider the problem of shape reconstruction from an unorganized data set which has many important applications in medical imaging, scientific computing, reverse engineering and geometric modelling. The reconstructed surface is obtained by continuously deforming an initial surface following the Partial Differential Equation (PDE)-based diffusion model derived by a minimal volume-like variational formulation. The evolution is driven both by the distance from the data set and by the curvature analytically computed by it. The distance function is computed by implicit local interpolants defined in terms of radial basis functions. Space discretization of the PDE model is obtained by finite co-volume schemes and semi-implicit approach is used in time/scale. The use of a level set method for the numerical computation of the surface reconstruction allows us to handle complex geometry and even changing topology,without the need of user-interaction. Numerical examples demonstrate the ability of the proposed method to produce high quality reconstructions. Moreover, we show the effectiveness of the new approach to solve hole filling problems and Boolean operations between different data sets.

Spatiotemporal characteristics of ultraviolet solar radiation in China

UV radiation plays an important role in climate change and photochemical reactions,and in Ecosystem Research.In this study,the authors presented study results of China’s National Basic Research Program Study on the climatic characteristics and reconstruction method of UV radiation in China.The spatiotemporal variation of UV radiation in China has been discussed,and then an effcient modeling method has been established to obtain history UV radiation data to analyse the variation trends of UV radiation in China.Finally,the influence of aerosol,cloud,ozone,and water vapor on UV radiation has been discussed.

DETECTING CEREBRAL ARTERIES AND VEINS:FROM LARGE TO SMALL

In this paper,a model-based reconstruction technique is proposed to simultaneously measure the relative deoxyhemoglobin concentration and the relative blood flow velocity in cerebral cortex.With the help of this model-based reconstruction technique,artifacts due to nonuniform laser illumination and curvature of cortex are efficiently corrected.The results of relative deoxyhemoglobin concentration and relative blood flow velocity are then used to detect and distinguish cerebral arteries and veins.In an experimental study on rat,cerebral blood vessels are segmented from the reconstructed blood flow image by Otsu multiple threshold method.Afterwards,arteries and veins are distinguished by a simple fuzzy criterion based on the information of relative deoxyhemoglobin concentration.

RECONSTRUCTION OF LAYER DATA WITH DEFORMABLE B-SPLINES

A new B-spline surface reconstruction method from layer data based on deformable model is presented. An initial deformable surface, which is represented as a closed cylinder, is firstly given. The surface is subject to internal forces describing its implicit smoothness property and external forces attracting it toward the layer data points. And then finite element method is adopted to solve its energy minimization problem, which results a bicubic closed B-spline surface with C^2 continuity. The proposed method can provide a smoothness and accurate surface model directly from the layer data, without the need to fit cross-sectional curves and make them compatible. The feasibility of the proposed method is verified by the experimental results.

A matching algorithm between precursory 3D process model and 2D working procedure drawing based on subgraph isomorphism

To meet the urgent requirement of enterprises for three-dimensional (3D) process models, an approach based on subgraph isomorphism is proposed to solve the matching problem between precursory 3D process model and 2D working procedure drawings. First, the projection drawings of the precursory 3D process model are obtained, then the primitives are extracted and the attributed adjacency graph (AAG) is constructed. Finally, by taking the 2D working procedure drawing as the AAG, and the projection drawing as the whole AAG, the matching problem between precursory 3D process model and 2D working procedure drawings is translated into the problem of subgraph isomorphism. To raise the matching efficiency, the AAG is partitioned, and the vertexes of the graph are classified effectively using the vertex’s attributes. Experimental results show that this method is able to support exact match and the matching efficiency can meet the requirement of practical applications.

Model reconstruction for worn blades based on hybrid surface registrations

Model reconstruction is crucial in blade repair because it directly determines the shape precision and finish of a repaired surface.However,owing to insufficient surface data pertaining to defective regions and the unique deformation caused by harsh environments,modeling a worn blade remains difficult.Hence,a model reconstruction method for worn blades is developed in this study.Unlike conventional methods of constructing and interpolating sectional curves,the proposed method focuses on modifying a nominal computer aided design(CAD)model to reconstruct the worn blade.Through weighted rigid registration and constraint-based non-rigid registration,the design surface extracted from the nominal CAD model can be deformed to align with the surface data of the worn blade without a significant loss of its initial shape.Verification results show that the deformed design surface exhibits sufficient smoothness and accuracy for guiding tool path generation in the subsequent blade repair.

Isogeometric analysis based on geometric reconstruction models

In isogeometric analysis(IGA),the boundary representation of computer-aided design(CAD)and the tensor-product non-uniform rational B-spline structure make the analysis of three-dimensional(3D)problems with irregular geometries difficult.In this paper,an IGA method for complex models is presented by reconstructing analysis-suitable models.The CAD model is represented by boundary polygons or point cloud and is embedded into a regular background grid,and a model reconstruction method is proposed to obtain the level set function of the approximate model,which can be directly used in IGA.Three 3D examples are used to test the proposed method,and the results demonstrate that the proposed method can deal with complex engineering parts reconstructed by boundary polygons or point clouds.

A landslide monitoring method using data from unmanned aerial vehicle and terrestrial laser scanning with insufficient and inaccurate ground control points

Non-contact remote sensing techniques,such as terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)photogrammetry,have been globally applied for landslide monitoring in high and steep mountainous areas.These techniques acquire terrain data and enable ground deformation monitoring.However,practical application of these technologies still faces many difficulties due to complex terrain,limited access and dense vegetation.For instance,monitoring high and steep slopes can obstruct the TLS sightline,and the accuracy of the UAV model may be compromised by absence of ground control points(GCPs).This paper proposes a TLS-and UAV-based method for monitoring landslide deformation in high mountain valleys using traditional real-time kinematics(RTK)-based control points(RCPs),low-precision TLS-based control points(TCPs)and assumed control points(ACPs)to achieve high-precision surface deformation analysis under obstructed vision and impassable conditions.The effects of GCP accuracy,GCP quantity and automatic tie point(ATP)quantity on the accuracy of UAV modeling and surface deformation analysis were comprehensively analyzed.The results show that,the proposed method allows for the monitoring accuracy of landslides to exceed the accuracy of the GCPs themselves by adding additional low-accuracy GCPs.The proposed method was implemented for monitoring the Xinhua landslide in Baoxing County,China,and was validated against data from multiple sources.