Reconstruction Technology Research Articles

Development of a 3D point cloud reconstruction-based apple canopy liquid sedimentation model

China leads the world in apple production, contributing to over half of the global total. Annually, extensive pesticide use is employed in apple cultivation, but the uncertain quantities of pesticide deposition and indiscriminate application have resulted in significant pesticide loss and residue. This poses a considerable potential risk to human health. This paper focuses on ground-based plant protection and stratifies the canopy horizontally, using 3D point cloud reconstruction technology and tree deposition experiments to explore the relationship between canopy leaf area index and pesticide deposition. By calculating the droplet penetration coefficient based on the amount of solution deposited at various canopy layers, a functional model linking leaf area index and droplet penetration coefficient has been established, with a correlation coefficient (R2) of 0.92. Based on this model, and by integrating the canopy volume with the amount of solution deposited on the outermost layer of the canopy, the total solution deposition on the entire tree can be estimated. According to the deposition model, there is a negative correlation between the amount of solution deposited on the canopy and the leaf area index of the canopy. Moreover, the experiments have shown that when the spray vehicle travels at a speed of 1.5 m/s, the maximum amount of solution is deposited while still ensuring an effective deposition rate. The establishment of the deposition estimation model allows for the visualization of canopy solution deposition and can guide fruit farmers in determining the appropriate frequency of spraying based on the severity of pest and disease infestations.

관상동맥 CT 조영 검사 시, 초 고해상도 딥러닝 알고리즘이 적용된 스텐트 영상의 진단적 가치

Recently, with the advancement of artificial intelligence image reconstruction technology, we are attempting to evaluate the accuracy of coronary artery stent lumen by developing ultra-high-resolution deep learning image reconstruction technology based on the most accurate 0.25mm multi-detector available. This study was divided into patient studies and phantom studies. Images were reconstructed in the same patient using four different methods: filter correction reverse projection, repetitive reconstruction, deep learning reconstruction, and artificial intelligence ultra-high resolution reconstruction. To objectively evaluate image quality, four parameters, image noise, CT density, signal-to-noise ratio, and contrast-to-noise ratio, were analyzed for each dataset. The phantom study installed a latex Nelaton sterilized catheter to express the coronary artery in the dummy chest phantom. Subsequently, a coronary stent (2.5mm and 3.5mm in diameter) was inserted into each catheter. Images were obtained using various reconstruction techniques (FBP, IR, DLR, and UHR-DLR) for each CT manufacturer. Stent clarity was evaluated using edge rise distance (ERD) and edge rise slope (ERS). We showed that the average Signal-to-Noise Ratio (SNR) and Contrast-to-Noise Ratio (CNR) of coronary arteries reconstructed with Ultra-High Resolution Deep Learning Reconstruction (UHR-DLR) improved by 29.2% and 36.4% compared to those reconstructed with DLR, respectively. Image noise was reduced by about 21.4% in UHR-DLR. The ERS average value of the coronary stent phantom measured by the CT manufacturers was highest in the UHR-DLR of Aquilion ONE Prism (Canon), and the ERD value is statistically significant for each CT manufacturer. Compared to FBP, HIR, and DLR, UHR-DLR not only improved image clarity and reduced image noise and blooming artifacts but also clearly showed the stent support structure and stent lumen.

Optimizing the performance of iron coke by coal blending: Insights from the metallurgical properties and structural characteristics

Iron coke has gained widespread attention for its advantages in carbon reduction and efficient resource utilization. However, the structure and strength of iron coke have constrained its further advancement. This study systematically explores the impact of different coal blending structures on the three-dimensional structure and tensile strength of iron coke through three-dimensional reconstruction technology. It highlights the improvement mechanism of 1/3 coking coal addition on the microstructure and metallurgical properties of iron coke. The study shows that the tensile strength of iron coke increases with the addition of coking coal, while lean coal exhibits the opposite trend. The addition of an appropriate proportion of 1/3 coking coal (20–22 %) enhances the tensile strength and mechanical strength of iron coke, reaching the level of second-grade coke. However, an addition exceeding 22 % leads to increased porosity and reduced strength due to the high volatile content. Finite element analysis demonstrates that stress is primarily localized in regions with thinner pore walls and at the iron-carbon interface, and the aggregation effect of iron particles further causes stress concentration. Iron coke reactivity initially diminishes and subsequently rises with the addition of 1/3 coking coal, and it shows a linear negative correlation with the strength after reaction. Structural evolution reveals that the addition of 1/3 coking coal reduces the orderliness of carbon microcrystals, thereby increasing the active sites for gasification reactions. Meanwhile, the initial reduction followed by an increase in the specific surface area of iron coke can impact adsorption sites, thereby influencing reaction activity. These findings not only provide a new understanding of the structure-performance relationship of iron coke but also offer practical guidance for iron coke production.

EIDNet: Extragradient-based iterative denoising network for image compressive sensing reconstruction

Compressive sensing (CS) is a kind of hardware-friendly technology in effective image reconstruction. Most of the existing CS reconstruction methods can be classified into model-driven iterative optimization methods and data-driven neural network methods. These two types of methods have their own problems with computational complexity and theoretical interpretability. Motivated by the deep unfolding theory, we develop the model-driven Extragradient-based Iterative Denoising Network, dubbed EIDNet, which is based on the denoising model of the iterative shrinkage threshold algorithm (ISTA). By unfolding the iterative denoising model into the deep learning network, EIDNet addresses the interpretability problem of neural network methods in CS reconstruction. Each denoising iteration of the optimization algorithm is mapped into one layer of the reconstruction network in EIDNet. The extragradient method is adopted to accelerate network convergence. In addition, we introduce a learning-based sampling and initialization network for adaptive sampling and initialization. Extensive experiments show that the proposed EIDNet can obtain better image CS reconstruction performance than other state-of-the-art CS methods while maintaining satisfactory reconstruction efficiency.

Nonlinear dynamics study of freestanding triboelectric nanogenerator system

As a nonlinear dynamic system, the dynamic equation of triboelectric nanogenerator system is not only difficult to accurately establish, but also difficult to solve. The output signals of triboelectric nanogenerator system depends on the initial conditions of the system, which reflects the dynamic behavior of the system. Currently, there is little research on using the output signals to study the dynamic behavior of triboelectric nanogenerator system. Therefore, a dynamic simulation model of a freestanding triboelectric nanogenerator (F-TENG) system was established using COMSOL software. The mathematical model of the F-TENG system was fitted. Using chaos theory and its phase-space reconstruction technology, the phase-space trajectories of the F-TENG system were reconstructed through voltage signals under external factors, and the dynamic behavior of the F-TENG system was discussed and studied. Finally, validation experiments were carried out. The research shows that with the variation of internal factors and operating frequency, the mathematical model of the F-TENG system remains basically unchanged, and the variation law of output characteristics also remains basically unchanged. The F-TENG system is in periodic motion; with the variation of external factors such as resistance and friction layer spacing, the mathematical model of the F-TENG system will evolve in a complex direction, and the output characteristics will gradually distort. The F-TENG system will evolve from periodic motion to chaotic motion. The research results not only reveal the dynamic behavior and enrich the nonlinear dynamic theory of triboelectric nanogenerator system, but also provide theoretical guidance for increasing the working bandwidth and stability of triboelectric nanogenerator system.

GridFormer: Point-Grid Transformer for Surface Reconstruction

Implicit neural networks have emerged as a crucial technology in 3D surface reconstruction. To reconstruct continuous surfaces from discrete point clouds, encoding the input points into regular grid features (plane or volume) has been commonly employed in existing approaches. However, these methods typically use the grid as an index for uniformly scattering point features. Compared with the irregular point features, the regular grid features may sacrifice some reconstruction details but improve efficiency. To take full advantage of these two types of features, we introduce a novel and high-efficiency attention mechanism between the grid and point features named Point-Grid Transformer (GridFormer). This mechanism treats the grid as a transfer point connecting the space and point cloud. Our method maximizes the spatial expressiveness of grid features and maintains computational efficiency. Furthermore, optimizing predictions over the entire space could potentially result in blurred boundaries. To address this issue, we further propose a boundary optimization strategy incorporating margin binary cross-entropy loss and boundary sampling. This approach enables us to achieve a more precise representation of the object structure. Our experiments validate that our method is effective and outperforms the state-of-the-art approaches under widely used benchmarks by producing more precise geometry reconstructions. The code is available at https://github.com/list17/GridFormer.

Case Study on Ecological Reconstruction Technology of Hollow Village in Mountainous Hilly Area

The ecological reconstruction of hollow village should gradually improve the rural living environment, regulate the features of farm houses and courtyards, beautify the village, protect and restore the village facilities such as ponds and ditches, natural landscape and pastoral landscape, and keep the overall style of the village in harmony with the natural environment. We will formulate plans for the protection and development of traditional villages, protect traditional culture, improve the lists of historic and cultural villages, traditional villages and residential houses, and establish and improve mechanisms for protection and supervision. The development of leisure agriculture, rural tourism, cultural and creative industries, the use of small town infrastructure and commercial service facilities, the overall drive to improve the quality of rural living environment.

The utilization of three-dimensional imaging and three-dimensional-printed model in autologous microtia reconstruction.

The use of three-dimensional (3D) technology helps surgeons in performing autologous microtia reconstruction due to more accurate measurements and a better precision template model. However, the technical aspects of using a 3D imaging and 3D-printed model and the difference in outcomes postoperatively remain poorly reviewed. This systematic review aimed to provide the current evidence of the benefit and technical aspects of using 3D technology in autologous microtia reconstruction. A systematic literature search was conducted across multiple databases: Medline, Embase, Google Scholar, and Central until June 2022. Studies that evaluated the use of 3D imaging or 3D-printed models for autogenous microtia reconstruction were selected. The quality of the included studies was also assessed with respect to the study design. A systematic literature search yielded 17 articles with a combination of observational and case report studies. Overall, 3D imaging showed a precise measurement for preoperative costal cartilage assessment. Compared to the 2D template, the utilization of a 3D-printed template provided a higher similarity rate relative to the unaffected ear, higher patient and surgeon satisfaction, and lower surgical time. Most 3D templates were fabricated using polylactic acid material on fused deposition modelling printers. The template costs were ranging from $1 to $4.5 depending on the material used. 3D imaging and 3D-printed templates could improve the outcome of autologous microtia reconstruction. However, the quality of the existing evidence remains low due to the heterogeneity of the reported outcomes. Further studies with more adequate comparability and defined outcomes are still required.

Based on 3D Virtual Reconstruction of Modern City Landscape Sculpture Planning Design

INTRODUCTION: With the continuous advancement of urbanization, urban landscape sculpture plays an increasingly important role in modern urban planning. Traditional planning and design methods make it challenging to demonstrate the three-dimensional sense and artistry of sculpture fully; therefore, this study explores a new method of planning and designing modern urban landscape sculpture based on three-dimensional virtual reconstruction.OBJECTIVES: This study aims to enhance the three-dimensional sense and artistry of urban landscape sculpture planning and design through three-dimensional virtual reconstruction technology to meet the needs of modern urban development better. By using advanced technical means, the planning and design can be made more intuitive and specific and provide urban residents with a more artistic public space.METHODS: The study adopts advanced three-dimensional virtual reconstruction technology, combined with urban planning and design theory, to plan and design modern urban landscape sculpture. Firstly, relevant literature on urban planning and sculpture design is collected to understand the existing design concepts and technical means. Secondly, a detailed virtual reconstruction of the sculpture is carried out by using three-dimensional modeling software to show the three-dimensional effect of the sculpture. Finally, the design scheme is optimized and improved through fieldwork and expert review.RESULTS: Through three-dimensional virtual reconstruction technology, this study successfully shows the whole picture of modern urban landscape sculpture. The design scheme not only has a three-dimensional sense but it has also been improved in artistry. The results of fieldwork and expert evaluation show that the new design scheme is more in line with the needs of urban development and adds a unique artistic atmosphere to the urban space.CONCLUSION: This study has achieved positive results in the field of modern urban landscape sculpture planning and design through 3D virtual reconstruction technology. The new design method not only provides a more specific tool for urban planners but also creates a more creative and artistic public space for urban residents. In the future, the application of this method in different urban contexts can be further explored and expanded to inject more innovation and vitality into urban planning and sculpture design.

Research on temperature field reconstruction technology for single-dome combustion chambers based on flame image

In order to compensate for the limitations of traditional temperature measurement methods, glass plates were installed on both sides of the single-dome combustion chamber test piece, and two CCD cameras observed the combustion flame in the furnace from different positions. The obtained flame radiation images were combined into a single image through the video capture card, and then the images were input into the reconstruction algorithm software to calculate the three-dimensional temperature field in the combustion chamber in real-time. The software could dynamically display the temperature distribution at three different positions in the chamber. This marked the first time that the temperature field in the combustion chamber was obtained using this new temperature measurement technology, which preliminarily verifies the feasibility of reconstructing the temperature field in the single-dome combustion chamber through flame image temperature measurement technology.

Exploring the Impact of 3D Printing on Advancing Reconstructive Surgery: A Comprehensive Review

This paper discusses the synergy between reconstructive surgery and 3D printing technology in the medical field. Reconstructive surgery aims to restore both physical and psychological well-being by addressing functional impairments caused by trauma, birth defects, or illnesses. It stands out as a specialty dedicated to restoring missing or damaged body parts, offering hope and healing to patients. On the other hand, 3D printing, an additive manufacturing technique, creates three-dimensional objects layer by layer using various materials. In medicine, it is employed to craft intricate scaffolds resembling tissue or organ structures, aiding in tissue regeneration. While not always faster than conventional methods, 3D printing accelerates the production of medical components and equipment, allowing for patient-specific customization. The trend towards decentralized manufacturing, including point-of-care 3D printing facilities in medical settings, enables customisation for each patient, potentially saving time and resources. This convergence of reconstructive surgery and 3D printing represents a significant advancement in patient care, offering tailored solutions and improving overall quality of life.

Investigating the impact of hyperspectral reconstruction techniques on the quantitative inversion of rice physiological parameters: A case study using the MST++ model

Quantitative inversion is a significant topic in remote sensing science. The development of visible light-based hyperspectral reconstruction techniques has opened up novel prospects for low-cost, high-precision remote sensing inversion in agriculture. The aim of this study was to assesses the effectiveness of hyperspectral reconstruction technology in agricultural remote sensing applications. Hyperspectral images were reconstructed using the MST++ hyperspectral reconstruction model and compared with the original visible light images in terms of their correlation with physiological parameters, the accuracy of single-feature modeling, and the accuracy of combined feature modeling. The results showed that compared to the visible light image, the reconstructed data exhibited a stronger correlation with physiological parameters, and the accuracy was improved in both the single-feature and the combined feature inversion mode. However, compared to multispectral sensors, hyperspectral reconstruction provided limited improvement on the inversion model accuracy. It was concluded that for physiological parameters that are not easy to be directly observed, deep mining of features in visible light. data through hyperspectral reconstruction technology can improve the accuracy of the inversion model. Appropriate feature selection and simple models are more suitable for the remote sensing inversion task of traditional agronomic plot experiments. To strengthen the application of hyperspectral reconstruction technology in agricultural remote sensing, further development is necessary with broader wavelength ranges and more diverse agricultural scenes.

Auxiliary active noise control system based on signal reconstruction

Faced with the problem of existing active control systems failing to obtain a reference signal and reducing or deteriorating the effectiveness of interior noise control, an Auxiliary Active Noise Control system (AANC) based on signal reconstruction technology is proposed, which main contains offline update of multi-network weights and online AANC controller. Firstly, considering the non-linear and non-stationary of the interior noise, and aiming to balance the modeling efficiency and accuracy of algorithms, based on the data fusion and compression sensing technology, a signal reconstruction model for multi-networks based on decomposition optimization is design; Then, in the online AANC controller, the offline calculation of the weight of signal reconstruction model is used to obtain the reconstruction reference signal of the control position, and based on the reconstructed signal, the controller achieves adaptive suppression of passenger ear-sides noise through the VSS-LMS algorithm. Finally, the effectiveness of the proposed AANC is verified using real vehicle data. The results show that the proposed system achieves the balance the modeling efficiency and accuracy, and is superior to existing ANC system in terms of robustness, guaranteeing the stable operation of the interior noise control.

An adaptive extended finite element based crack propagation analysis method

In this paper, a method of crack propagation analysis based on adaptive extension finite element is proposed. This method combines adaptive mesh reconstruction technology with extended finite element method (XFEM). Firstly, the model of engineering structure is discretized with the help of mesh generation software, and the initial mesh is divided. Secondly, Construction of XFEM model, and the tip of crack strengthening function is introduced to describe the physical field properties of the crack tip. The integral equation is solved to obtain the crack tip parameters. Then, the adaptive mesh reconstruction technology is built to refine the mesh of crack tip area through the error estimation of crack tip. Finally, the SIFs at the crack tip were calculated using the interaction integral, and the path direction of crack growth was determined using the maximum circumferential tensile stress criterion. Thus, the propagation path can be well traced.

Large-Scale 3D Reconstruction from Multi-View Imagery: A Comprehensive Review

Three-dimensional reconstruction is a key technology employed to represent virtual reality in the real world, which is valuable in computer vision. Large-scale 3D models have broad application prospects in the fields of smart cities, navigation, virtual tourism, disaster warning, and search-and-rescue missions. Unfortunately, most image-based studies currently prioritize the speed and accuracy of 3D reconstruction in indoor scenes. While there are some studies that address large-scale scenes, there has been a lack of systematic comprehensive efforts to bring together the advancements made in the field of 3D reconstruction in large-scale scenes. Hence, this paper presents a comprehensive overview of a 3D reconstruction technique that utilizes multi-view imagery from large-scale scenes. In this article, a comprehensive summary and analysis of vision-based 3D reconstruction technology for large-scale scenes are presented. The 3D reconstruction algorithms are extensively categorized into traditional and learning-based methods. Furthermore, these methods can be categorized based on whether the sensor actively illuminates objects with light sources, resulting in two categories: active and passive methods. Two active methods, namely, structured light and laser scanning, are briefly introduced. The focus then shifts to structure from motion (SfM), stereo matching, and multi-view stereo (MVS), encompassing both traditional and learning-based approaches. Additionally, a novel approach of neural-radiance-field-based 3D reconstruction is introduced. The workflow and improvements in large-scale scenes are elaborated upon. Subsequently, some well-known datasets and evaluation metrics for various 3D reconstruction tasks are introduced. Lastly, a summary of the challenges encountered in the application of 3D reconstruction technology in large-scale outdoor scenes is provided, along with predictions for future trends in development.

Robot-Assisted Free Flap Reconstruction for Oropharyngeal Cancer: A Systematic Review

Management of oropharyngeal cancer is undergoing a shift due to different profiles of patients, who tend to be younger and show better prognosis. Transoral robotic surgery is being established as the first-line approach as the procedure has shown minimal invasiveness and good oncological and functional outcomes, while avoiding extensive open surgery approaches and intensive chemoradiotherapy treatments. Naturally, the next step in the management of oropharyngeal cancer would be using the same technology for reconstruction to preserve the function and anatomy of the oropharynx. Free flaps, especially the radial forearm free flap and the anterolateral thigh flap, proved to be ideal for oropharyngeal reconstruction, and robotic assistance in these cases is yet to be explored. A limited number of studies have reported robot-assisted oropharyngeal reconstruction, so this review summarizes the currently available data and explores the feasibility, safety, and functional outcomes of this novel technique.

Research on Three-dimensional Reconstruction

Nowadays, with the continuous development of the field of computer vision and computer graphics, 3D reconstruction technology has become an area that has attracted much of people’s attention. 3D reconstruction refers to creating a 3D model of an object in the real world by extracting information from multiple 2D images or sensor data. This technology has important usages in many fields, including virtual reality, augmented reality, cultural heritage preservation, medical image processing, industrial design, etc. This thesis examines the application of 3D reconstruction in various fields, discusses the definition of development history, its wide application in life, and its advantages and disadvantages. This article is divided into four parts and discusses the algorithm research of 3D reconstruction in four fields. Through an in-depth study of 3D reconstruction technology, we can better understand its role in different fields and provide strong support for developing and innovating related applications. As more accurate devices and algorithms continue to advance, 3D reconstruction technology is expected to show its significant potential in more and more fields and create more realistic and useful digital experiences for us.

Multi-Objective Dynamic Reconstruction of Distributed Energy Distribution Networks Based on Stochastic Probability Models and Optimized Beetle Antennae Search

In the dynamic optimization problem of the distribution network, a dynamic reconstruction method based on a stochastic probability model and optimized beetle antennae search is proposed. By implementing dynamic reconstruction of distributed energy distribution networks, the dynamic regulation and optimization capabilities of the distribution network can be improved. In this study, a random probability model is used to describe the uncertainty in the power grid. The beetle antennae search is used for dynamic multi-objective optimization. The performance of the beetle antennae search is improved by combining it with the simulated annealing algorithm. According to the results, the optimization success rate of the model was 98.7%. Compared with the discrete binary particle swarm optimization algorithm and bacterial foraging optimization algorithm, it was 9.3% and 26.1% faster, respectively. For practical applications, this model could effectively reduce power grid transmission losses, with a reduction range of 16.7–18.6%. Meanwhile, the charging and discharging loads were effectively reduced, with a reduction range of 16.2–19.7%. Therefore, this method has significant optimization effects on actual power grid operation. This research achievement contributes to the further development of dynamic reconstruction technology for distribution networks, improving the operational efficiency and stability of the power grid. This has important practical significance for achieving green and intelligent operation of the power system.

Research on Indoor 3D Reconstruction Technology Based on Semantic Visual Simultaneous Localization and Mapping

In response to the challenge that traditional visual simultaneous localization and mapping (SLAM) systems, based on the assumption of a static environment, struggle to achieve real-time indoor 3D reconstruction in complex dynamic scenes, this paper proposes a real-time indoor 3D reconstruction algorithm based on semantic visual SLAM. By leveraging object detection to obtain 2D semantic information and providing prior information for geometric methods, the fusion of the two effectively suppresses dynamic features, reduces reliance on deep learning methods, and ensures the algorithm's real-time performance. Experimental results on dynamic scenes in the TUM RGB-D dataset show that our algorithm maintains nearly unchanged real-time performance while achieving an average performance improvement of approximately 97.56% and 97.31% on the TUM dataset and Bonn dataset, respectively, compared to the ORB-SLAM2 system. Moreover, our algorithm can reconstruct more intuitive indoor global Octo-map and semantic metric maps compared to sparse point cloud maps, effectively enhancing the scene perception capability of mobile robots and laying the foundation for performing advanced tasks. Furthermore, our algorithm demonstrates a 3.5-10.5 times improvement in real-time performance compared to other mainstream semantic SLAM systems. Experimental results on the NVIDIA Jetson AGX Xavier confirm that our algorithm can run in real time on low-power platforms such as mobile robots or drones. However, the drawbacks of our algorithm include lower reconstruction accuracy in low-texture and large-scale scenes and ineffective suppression of dynamic features in low-dynamic scenes. Future work will consider replacing and improving deep learning methods and integrating IMU and other sensors to enhance system usability.

Multi-View Jujube Tree Trunks Stereo Reconstruction Based on UAV Remote Sensing Imaging Acquisition System

High-quality agricultural multi-view stereo reconstruction technology is the key to precision and informatization in agriculture. Multi-view stereo reconstruction methods are an important part of 3D vision technology. In the multi-view stereo 3D reconstruction method based on deep learning, the effect of feature extraction directly affects the accuracy of reconstruction. Aiming at the actual problems in orchard fruit tree reconstruction, this paper designs an improved multi-view stereo structure based on the combination of remote sensing and artificial intelligence to realize the accurate reconstruction of jujube tree trunks. Firstly, an automatic key frame extraction method is proposed for the DSST target tracking algorithm to quickly recognize and extract high-quality data. Secondly, a composite U-Net feature extraction network is designed to enhance the reconstruction accuracy, while the DRE-Net feature extraction enhancement network improved by the parallel self-attention mechanism enhances the reconstruction completeness. Comparison tests show different levels of improvement on the Technical University of Denmark (DTU) dataset compared to other deep learning-based methods. Ablation test on the self-constructed dataset, the MVSNet + Co U-Net + DRE-Net_SA method proposed in this paper improves 20.4% in Accuracy, 12.8% in Completion, and 16.8% in Overall compared to the base model, which verifies the real effectiveness of the scheme.