Digital Restoration of Historical Buildings by Integrating 3D PC Reconstruction and GAN Algorithm

Historical architecture is an important carrier of cultural and historical heritage in a country and region,and its protection and restoration work plays a crucial role in the inheritance of cultural heritage.However,the damage and destruction of buildings urgently need to be repaired due to the ancient age of historical buildings and the influence of natural environment and human factors.Therefore,an artificial intelligence repair technology based on three-dimensional(3D)point cloud(PC)reconstruction and generative adversarial networks(GANs)was proposed to improve the precision and efficiency of repair work.First,in-depth research on the principles and algorithms of 3D PC data processing and GANs should be conducted.Second,a digital restoration frameworkwas constructed by combining these two artificial intelligence technologies to achieve precise and efficient restoration of historical buildings through continuous adversarial learning processes.The experimental results showed that the errors in the restoration of palace buildings,defense walls,pagodas,altars,temples,and mausoleums were 0.17,0.12,0.13,0.11,and 0.09,respectively.The technique can significantly reduce the error while maintaining the high-precision repair effect.This technology with artificial intelligence as the core has excellent accuracy and stability in the digital restoration.It provides a new technical means for the digital restoration of historical buildings and has important practical significance for the protection of cultural heritage.

Three-dimensional(3D)parametric measurements of individual gravels in the Gobi region using point cloud technique

Gobi spans a large area of China,surpassing the combined expanse of mobile dunes and semi-fixed dunes.Its presence significantly influences the movement of sand and dust.However,the complex origins and diverse materials constituting the Gobi result in notable differences in saltation processes across various Gobi surfaces.It is challenging to describe these processes according to a uniform morphology.Therefore,it becomes imperative to articulate surface characteristics through parameters such as the three-dimensional(3D)size and shape of gravel.Collecting morphology information for Gobi gravels is essential for studying its genesis and sand saltation.To enhance the efficiency and information yield of gravel parameter measurements,this study conducted field experiments in the Gobi region across Dunhuang City,Guazhou County,and Yumen City(administrated by Jiuquan City),Gansu Province,China in March 2023.A research framework and methodology for measuring 3D parameters of gravel using point cloud were developed,alongside improved calculation formulas for 3D parameters including gravel grain size,volume,flatness,roundness,sphericity,and equivalent grain size.Leveraging multi-view geometry technology for 3D reconstruction allowed for establishing an optimal data acquisition scheme characterized by high point cloud reconstruction efficiency and clear quality.Additionally,the proposed methodology incorporated point cloud clustering,segmentation,and filtering techniques to isolate individual gravel point clouds.Advanced point cloud algorithms,including the Oriented Bounding Box(OBB),point cloud slicing method,and point cloud triangulation,were then deployed to calculate the 3D parameters of individual gravels.These systematic processes allow precise and detailed characterization of individual gravels.For gravel grain size and volume,the correlation coefficients between point cloud and manual measurements all exceeded 0.9000,confirming the feasibility of the proposed methodology for measuring 3D parameters of individual gravels.The proposed workflow yields accurate calculations of relevant parameters for Gobi gravels,providing essential data support for subsequent studies on Gobi environments.

RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding regions.The sensor nodes are responsible for accumulating and exchanging information.Generally,node local-ization is the process of identifying the target node’s location.In this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization models.Initially,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D WSN.The position of the anchor nodes is fixed for detecting the location of the target.Further,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node localization.In the regular and irregular surfaces,this hybrid algorithm effectively performs the localization process.The suggested hybrid algorithm converges very fast in the three-dimensional(3D)environment.The accuracy of the proposed node localization process is 94.25%.

The parallel 3D magnetotelluric forward modeling algorithm

The workload of the 3D magnetotelluric forward modeling algorithm is so large that the traditional serial algorithm costs an extremely large compute time. However, the 3D forward modeling algorithm can process the data in the frequency domain, which is very suitable for parallel computation. With the advantage of MPI and based on an analysis of the flow of the 3D magnetotelluric serial forward algorithm, we suggest the idea of parallel computation and apply it. Three theoretical models are tested and the execution efficiency is compared in different situations. The results indicate that the parallel 3D forward modeling computation is correct and the efficiency is greatly improved. This method is suitable for large size geophysical computations.

Optimization algorithm for rapid 3D gravity inversion

The practical application of 3D inversion of gravity data requires a lot of computation time and storage space.To solve this problem,we present an integrated optimization algorithm with the following components:(1)targeting high accuracy in the space domain and fast computation in the wavenumber domain,we design a fast 3D forward algorithm with high precision;and(2)taking advantage of the symmetry of the inversion matrix,the main calculation in gravity conjugate gradient inversion is decomposed into two forward calculations,thus optimizing the computational efficiency of 3D gravity inversion.We verify the calculation accuracy and efficiency of the optimization algorithm by testing various grid-number models through numerical simulation experiments.

Using MC Algorithm to Implement 3D Image Reconstruction for Yunnan Weather Radar Data

3D image reconstruction for weather radar data can not only help the weatherman to improve the forecast efficiency and accuracy, but also help people to understand the weather conditions easily and quickly. Marching Cubes (MC) algorithm in the surface rendering has more excellent applicability in 3D reconstruction for the slice images;it may shorten the time to find and calculate the isosurface from raw volume data, reflect the shape structure more accurately. In this paper, we discuss a method to reconstruct the 3D weather cloud image by using the proposed Cube Weighting Interpolation (CWI) and MC algorithm. Firstly, we detail the steps of CWI, apply it to project the raw radar data into the cubes and obtain the equally spaced cloud slice images, then employ MC algorithm to draw the isosurface. Some experiments show that our method has a good effect and simple operation, which may provide an intuitive and effective reference for realizing the 3D surface reconstruction and meteorological image stereo visualization.

An improved 3D shape haptic rendering algorithm for finger mounted vibrotactile device

To improve the sense of reality on perception, an improved algorithm of 3D shape haptic rendering is put forward based on a finger mounted vibrotactile device. The principle is that the interactive information and the shape information are conveyed to users when they touch virtual objects at mobile terminals by attaching the vibrotactile feedback on a fingertip. The extraction of shape characteristics, the interactive information and the mapping of shape in formation of vibration stimulation are key parts of the proposed algorithm to realize the real tactile rendering. The contact status of the interaction process, the height information and local gradient of the touch point are regarded as shape information and used to control the vibration intension, rhythm and distribution of the vibrators. With different contact status and shape information, the vibration pattern can be adjusted in time to imitate the outlines of virtual objects. Finally, the effectiveness of the algorithm is verified by shape perception experiments. The results show that the improved algorithm is effective for 3D shape haptic rendering.

Improvement of Binocular Reconstruction Algorithm for Measuring 3D Pavement Texture Using a Single Laser Line Scanning Constraint

The dense and accurate measurement of 3D texture is helpful in evaluating the pavement function.To form dense mandatory constraints and improve matching accuracy,the traditional binocular reconstruction technology was improved threefold.First,a single moving laser line was introduced to carry out global scanning constraints on the target,which would well overcome the difficulty of installing and recognizing excessive laser lines.Second,four kinds of improved algorithms,namely,disparity replacement,superposition synthesis,subregion segmentation,and subregion segmentation centroid enhancement,were established based on different constraint mechanism.Last,the improved binocular reconstruction test device was developed to realize the dual functions of 3D texture measurement and precision self-evaluation.Results show that compared with traditional algorithms,the introduction of a single laser line scanning constraint is helpful in improving the measurement’s accuracy.Among various improved algorithms,the improvement effect of the subregion segmentation centroid enhancement method is the best.It has a good effect on both overall measurement and single pointmeasurement,which can be considered to be used in pavement function evaluation.

A New 3D Wireless Directional Sensing Model and Coverage Enhancement Algorithm

Coverage control for each sensor is based on a 2D directional sensing model in directional sensor networks conventionally. But the 2D model cannot accurately characterize the real environment. In order to solve this problem,a new 3D directional sensor model and coverage enhancement algorithm is proposed. We can adjust the pitch angle and deviation angle to enhance the coverage rate. And the coverage enhancement algorithm is based on an improved gravitational search algorithm. In this paper the two improved strategies of GSA are directional mutation strategy and individual evolution strategy. A set of simulations show that our coverage enhancement algorithm has a good performance to improve the coverage rate of the wireless directional sensor network on different number of nodes,different virtual angles and different sensing radius.

A New Three-Dimensional(3D)Printing Prepress Algorithm for Simulation of Planned Surgery for Congenital Heart Disease

Background:Three-dimensional printing technology may become a key factor in transforming clinical practice and in significant improvement of treatment outcomes.The introduction of this technique into pediatric cardiac surgery will allow us to study features of the anatomy and spatial relations of a defect and to simulate the optimal surgical repair on a printed model in every individual case.Methods:We performed the prospective cohort study which included 29 children with congenital heart defects.The hearts and the great vessels were modeled and printed out.Measurements of the same cardiac areas were taken in the same planes and points at multislice computed tomography images(group 1)and on printed 3D models of the hearts(group 2).Pre-printing treatment of the multislice computed tomography data and 3D model preparation were performed according to a newly developed algorithm.Results:The measurements taken on the 3D-printed cardiac models and the tomographic images did not differ significantly,which allowed us to conclude that the models were highly accurate and informative.The new algorithm greatly simplifies and speeds up the preparation of a 3D model for printing,while maintaining high accuracy and level of detail.Conclusions:The 3D-printed models provide an accurate preoperative assessment of the anatomy of a defect in each case.The new algorithm has several important advantages over other available programs.They enable the development of customized preliminary plans for surgical repair of each specific complex congenital heart disease,predict possible issues,determine the optimal surgical tactics,and significantly improve surgical outcomes.

Fragile Watermarking of 3D Models Using Genetic Algorithms

This paper describes a novel algorithm for fragile watermarking of 3D models. Fragile watermarking requires detection of even minute intentional changes to the 3D model along with the location of the change. This poses a challenge since inserting random amount of watermark in all the vertices of the model would generally introduce perceptible distortion. The proposed algorithm overcomes this challenge by using genetic algorithm to modify every vertex location in the model so that there is no perceptible distortion. Various experimental results are used to justify the choice of the genetic algorithm design parameters. Experimental results also indicate that the proposed algorithm can accurately detect location of any mesh modification.

An improved bicubic imaging fitting algorithm for 3D radar detection target

3D ground-penetrating radar has been widely used in urban road underground disease detection due to its nondestructive,efficient,and intuitive results.However,the 3D imaging of the underground target body presents the edge plate phenomenon due to the space between the 3D radar array antennas.Consequently,direct 3D imaging using detection results cannot reflect underground spatial distribution characteristics.Due to the wide-beam polarization of the ground-penetrating radar antenna,the emission of electromagnetic waves with a specific width decreases the strong middle energy on both sides gradually.Therefore,a bicubic high-precision 3D target body slice-imaging fitting algorithm with changing trend characteristics is constructed by combining the subsurface target characteristics with the changing spatial morphology trends.Using the wide-angle polarization antenna’s characteristics in the algorithm to build the trend factor between the measurement lines,the target body change trend and the edge detail portrayal achieve a 3D ground-penetrating radar-detection target high-precision fitting.Compared with other traditional fitting techniques,the fitting error is small.This paper conducts experiments and analyses on GpaMax 3D forward modeling and 3D ground-penetrating measured radar data.The experiments show that the improved bicubic fitting algorithm can eff ectively improve the accuracy of underground target slice imaging and the 3D ground-penetrating radar’s anomaly interpretation.

Research on a bifurcation location algorithm of a drainage tube based on 3D medical images

Based on patient computerized tomography data,we segmented a region containing an intracranial hematoma using the threshold method and reconstructed the 3D hematoma model.To improve the efficiency and accuracy of identifying puncture points,a point-cloud search arithmetic method for modified adaptive weighted particle swarm optimization is proposed and used for optimal external axis extraction.According to the characteristics of the multitube drainage tube and the clinical needs of puncture for intracranial hematoma removal,the proposed algorithm can provide an optimal route for a drainage tube for the hematoma,the precise position of the puncture point,and preoperative planning information,which have considerable instructional significance for clinicians.

A Study on Parallel Computation Based on 3D Forward Algorithm of Gravity

Interpretation of geophysical material is the prospecting method. Interpretation of Gravity-megnetic data is based on data processing and inversion. When the grid is divided into several million cells, the computing task is heavy and time-consuming. In order to increase efficiency of the 3D forward modeling, the paper will adopt MPI parallel algorithm and the several processes will deal with data in the method. Finally, we can gather the result. Through comparing the result of sequence algorithm with the result of MPI parallel algorithm, we can see the result is the same. When the number of processes is 2 to 8, the speed-up ratio is 1.97 to 5. The MPI parallel algorithm is very efficient.

A Graphic Algorithm of＂Track＂ Effect in 3D Games

In 3D games, a lot of weapons in the movement will drag a ＂follow the shadow＂ effect, which is called the ＂track＂. In this paper, we first analyze the change rule of the ＂track＂, and then put forward a kind of algorithm to realize the ＂track＂. The calculation of this algorithm is small, but the effect is very real, has been successfully applied to a variety of 3D games.

3维Douglas-Peucker算法及其在DEM自动综合中的应用研究

在分析2维Douglas-Peucker算法原理实质的基础上,提出3维Douglas-Peucker算法,并将此法应用到对DEM的基础———3维离散点的自动综合上。采用计算机程序对新算法进行的验证,初步结果表明该算法能较好地筛选出DEM整体及局部范围的地貌特征点,其计算效率也较令人满意。

Parallel rapid relaxation inversion of 3D magnetotelluric data

We implement a parallel algorithm with the advantage of MPI （Message Passing Interface） to speed up the rapid relaxation inversion for 3D magnetotelluric data. We test the parallel rapid relaxation algorithm with synthetic and real data. The execution efficiency of the algorithm for several different situations is also compared. The results indicate that the parallel rapid relaxation algorithm for 3D magnetotelluric inversion is effective. This parallel algorithm implemented on a common PC promotes the practical application of 3D magnetotelluric inversion and can be suitable for the other geophysical 3D modeling and inversion.

基于改进3D SPIHT的MRI序列图像压缩方法

目的研究一种有效的MRI序列图像压缩方法。方法以2组不同数量、不同层厚的MRI序列图像为例,针对3D SPIHT算法运算复杂度,在对D型、L型表项重复判断的不足上,提出了一种改进的3DSPIHT方法;同时,根据MRI序列图像的相关性特点,提出了分组编/解码的方法,结合3D小波变换和应用改进的3D SPIHT方法,实现了MRI序列图像压缩。结果分组结合改进3D SPIHT方法与2DSPIHT,3D SPIHT相比,能够得到较好重构图像,同时,峰值信噪比(PSNR)提高了1～8 dB左右。结论在相同码率下,分组结合改进3D SPIHT的方法提高了PSNR和图像恢复质量,可以更好地解决大量MRI序列图像存储与传输问题。

基于拓扑结构不变性的3D并行细化算法及其应用

目的双距离场算法提取的骨架居中性不佳,并且算法复杂度高,实时性差。本文提出一种基于拓扑结构不变性的3D细化算法以提取肺气管的骨架。方法首先介绍了基于双距离场和拓扑结构不变性两种算法的基本原理,然后通过对欧拉特性不变性的证明,利用欧拉特性表查询来计算欧拉值,并计算26临域的连通度。利用欧拉特性值和连通度来保证拓扑结构,定义了一种拓扑结构不变的简单点,用并行的细化策略,快速获取单像素宽、连通的骨架中心线。结果将此算法应用于肺气管快速提取骨架中心线,利用已经分割出的高精度肺部气道树来提取骨架中心线。结论基于拓扑结构不变性的3D并行细化算法与基于距离场的骨架提取算法相比得到更光滑、居中性更好的骨架,并且鲁棒性好,对噪声不敏感。