Advanced Engineering Informatics最新文献_第4页

An integrated MEREC-taxonomy methodology using T-spherical fuzzy information: An application in smart farming decision analytics 使用T-球形模糊信息的综合MEREC-分类方法：智能农业决策分析中的应用

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102891

Ting-Yu Chen

This research presents an effective approach for multiple criteria decision analytics by integrating the MEthod based on the Removal Effects of Criteria (MEREC) and the taxonomy technique within the context of T-spherical fuzzy (T-SF) uncertainties. Firstly, a specialized score function tailored for T-spherical fuzziness is developed to enhance methodologies in managing uncertainty within decision-making processes. The T-SF MEREC methodology is then introduced, utilizing this score function to ascertain the objective importance of criteria in uncertain settings. Additionally, the taxonomy methodology is adapted to address decision-analytic challenges associated with T-spherical fuzziness, leveraging T-SF Minkowski distance measures and T-SF weighted averaging and geometric interaction operations. The study also formulates an integrated MEREC-taxonomy methodology to address complex decision-making challenges under T-SF uncertainty. To demonstrate practical utility, these methodologies are applied to smart farming decision analytics. Evaluating various operational models of smart farms in urban agriculture across multiple criteria, the study validates the effectiveness and applicability of the integrated techniques. This successful application underscores the robustness and versatility of the approach, affirming its capacity to enhance decision-making in complex and unpredictable situations.

本研究在 T 球形模糊（T-SF）不确定性的背景下，通过整合基于标准移除效应的 MEthod（MEREC）和分类技术，提出了一种有效的多标准决策分析方法。首先，针对 T-SF 模糊性开发了专门的评分函数，以加强决策过程中的不确定性管理方法。然后介绍了 T-SF MEREC 方法，利用该评分函数来确定不确定环境中标准的客观重要性。此外，利用 T-SF 明考斯基距离测量和 T-SF 加权平均与几何交互操作，对分类方法进行了调整，以应对与 T 球形模糊性相关的决策分析挑战。该研究还制定了一种综合的 MEREC 分类方法，以应对 T-SF 不确定性下的复杂决策挑战。为了证明这些方法的实用性，我们将其应用于智能农业决策分析。该研究根据多个标准对城市农业中智能农场的各种运营模式进行了评估，验证了综合技术的有效性和适用性。这一成功应用强调了该方法的稳健性和多功能性，肯定了其在复杂和不可预测的情况下加强决策的能力。

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引用次数: 0

UAV formation path planning for mountainous forest terrain utilizing an artificial rabbit optimizer incorporating reinforcement learning and thermal conduction search strategies 利用包含强化学习和热传导搜索策略的人工兔优化器进行山林地形无人机编队路径规划

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102947

Wentao Wang , Xiaoli Li , Jun Tian

The path planning of Unmanned Aerial Vehicle (UAV) formations plays a crucial role in mountainous forest monitoring missions. However, path planning is particularly challenging due to steep terrain and dense vegetation, making it difficult to generate optimal flight paths. The goal of UAV formation path planning in forest monitoring is to create safe, feasible flight paths for each UAV, avoiding terrain obstacles and ensuring coordination and safety, ultimately improving the quality of mission accomplishment. This study establishes a mathematical model that incorporates multiple constraints, such as flight distance, collision threats, and path stability, effectively transforming the complex problem of UAV formation path planning into an optimization problem. To address this multi-constraint path planning optimization problem, an Artificial Rabbit Optimization algorithm incorporating Reinforcement Learning and Thermal conduction search strategy (RLTARO) is proposed. The incorporation of multiple strategies aims to improve the balance of exploration and exploitation of the algorithms as well as algorithmic convergence in the face of complex path planning problems. The comprehensive comparison of the RLTARO algorithm with nine advanced algorithms of similar type in the CEC2017 suite demonstrates its outstanding convergence and robustness across various types of optimization problems. The results of path planning experiments conducted on six mountainous forest terrains with varying complexities demonstrate that RLTARO can efficiently and reliably plan flight paths for UAV formations. Furthermore, the Friedman test results from multiple experiments consistently indicate that RLTARO holds significant performance advantages over the comparison algorithms.

无人机编队的路径规划在山区森林监测任务中发挥着至关重要的作用。然而，由于地形陡峭、植被茂密，路径规划尤其具有挑战性，难以生成最佳飞行路径。森林监测中无人机编队路径规划的目标是为每架无人机创建安全可行的飞行路径，避开地形障碍，确保协调和安全，最终提高任务完成质量。本研究建立了一个包含飞行距离、碰撞威胁和路径稳定性等多重约束条件的数学模型，有效地将无人机编队路径规划这一复杂问题转化为优化问题。针对这一多约束路径规划优化问题，提出了一种融合了强化学习和热导搜索策略（RLTARO）的人工兔优化算法。在面对复杂的路径规划问题时，多种策略的结合旨在改善算法探索和利用的平衡以及算法的收敛性。RLTARO 算法与 CEC2017 套件中九种同类先进算法的综合比较表明，该算法在各类优化问题中具有出色的收敛性和鲁棒性。在六种复杂程度不同的山地森林地形上进行的路径规划实验结果表明，RLTARO 可以高效可靠地规划无人机编队的飞行路径。此外，来自多个实验的弗里德曼测试结果一致表明，RLTARO 与对比算法相比具有显著的性能优势。

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引用次数: 0

Identifying the structure of illicit supply chains with sparse data: A simulation model calibration approach 利用稀疏数据识别非法供应链的结构：模拟模型校准方法

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102926

Isabelle M. van Schilt , Jan H. Kwakkel , Jelte P. Mense , Alexander Verbraeck

Illicit supply chains for products like counterfeit Personal Protective Equipment (PPE) are characterized by sparse data and great uncertainty about the operational and logistical structure, making criminal activities largely invisible to law enforcement and challenging to intervene in. Simulation is a way to get insight into the behavior of complex systems, using calibration to tune model parameters to match its real-world counterpart. Calibration methods for simulation models of illicit supply chains should work with sparse data, while also tuning the structure of the simulation model. Thus, this study addresses the question: “To what extent can various model calibration techniques reconstruct the underlying structure of an illicit supply chain when varying the degree of data sparseness?” We evaluate the quality-of-fit of a reference technique, Powell’s Method, and three model calibration techniques that have shown promise for sparse data: Approximate Bayesian Computing, Bayesian Optimization, and Genetic Algorithms. For this, we use a simulation model of a stylized counterfeit PPE supply chain as ground truth. We extract data from this ground truth and systematically vary its sparseness. We parameterize structural uncertainty using System Entity Structure. The results demonstrate that Bayesian Optimization and Genetic Algorithms are suitable for reconstructing the underlying structure of an illicit supply chain for a varying degree of data sparseness. Both techniques identify a diverse set of optimal solutions that fit with the sparse data. For a comprehensive understanding of illicit supply chain structures, we propose to combine the results of the two techniques. Future research should focus on developing a combined algorithm and incorporating solution diversity.

假冒个人防护设备（PPE）等产品的非法供应链的特点是数据稀少，运营和物流结构具有很大的不确定性，这使得执法部门在很大程度上无法发现犯罪活动，也很难对其进行干预。仿真是一种深入了解复杂系统行为的方法，通过校准来调整模型参数，使其与现实世界中的模型相匹配。非法供应链仿真模型的校准方法应适用于稀疏数据，同时还能调整仿真模型的结构。因此，本研究探讨的问题是"当数据稀疏程度不同时，各种模型校准技术能在多大程度上重建非法供应链的基本结构？我们评估了一种参考技术 Powell's Method 和三种模型校准技术的拟合质量，这三种技术在稀疏数据方面表现出了良好的前景：近似贝叶斯计算、贝叶斯优化和遗传算法。为此，我们使用一个风格化的假冒 PPE 供应链仿真模型作为基本事实。我们从地面实况中提取数据，并系统地改变其稀疏程度。我们使用系统实体结构对结构不确定性进行参数化。结果表明，贝叶斯优化法和遗传算法适用于在数据稀疏程度不同的情况下重建非法供应链的底层结构。这两种技术都能找出符合稀疏数据的各种最优解。为了全面了解非法供应链结构，我们建议将两种技术的结果结合起来。未来的研究应侧重于开发一种组合算法，并将解决方案的多样性纳入其中。

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引用次数: 0

Optimal design of an integrated inspection scheme with two adjustable sampling mechanisms for lot disposition 采用两种可调抽样机制的批次处置综合检测方案的优化设计

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102845

To-Cheng Wang , Chien-Wei Wu

Acceptance sampling plans are statistical quality control methods commonly used to efficiently verify product quality under controlled risks. Recent research has developed the multiple dependent-state sampling plan (MDSP), which incorporates historical lot quality information, and the repetitive group sampling plan (RGSP), which allows for repeat sampling, to enhance the cost-effectiveness of sampling inspections. The modified RGSP (MRGSP) integrates the sampling mechanisms of both MDSP and RGSP. However, investigative analyses have uncovered significant deficiencies in the sampling mechanisms of MDSP and RGSP, with potential problems in MRGSP being even more severe. Therefore, this paper proposes an adjustable MRGSP (AMRGSP) based on unilateral process capability indices to establish a more adaptive and flexible sampling mechanism, reducing the limitations of MRGSP. We derive the operational characteristic function and average sample number function of AMRGSP, and establish a nonlinear optimization model considering Type I and II errors to determine the optimal plan design. Performance comparisons of the proposed AMRGSP with recent sampling plans revealed that the proposed plan offers reliable lot discriminative power and significantly reduces the sample size required for inspection, providing excellent cost-effectiveness. Finally, we evaluate the proposed plan using a practical case study to demonstrate its applicability in practice.

验收抽样计划是一种统计质量控制方法，通常用于在风险可控的情况下有效检验产品质量。最近的研究开发了结合历史批次质量信息的多依赖状态抽样计划（MDSP）和允许重复抽样的重复分组抽样计划（RGSP），以提高抽样检查的成本效益。修改后的 RGSP（MRGSP）整合了 MDSP 和 RGSP 的抽样机制。然而，调查分析发现 MDSP 和 RGSP 的抽样机制存在重大缺陷，而 MRGSP 的潜在问题更为严重。因此，本文提出了一种基于单边过程能力指数的可调整 MRGSP（AMRGSP），以建立一种更具适应性和灵活性的采样机制，减少 MRGSP 的局限性。我们推导出了 AMRGSP 的运行特性函数和平均采样数函数，并建立了一个考虑到 I 类和 II 类误差的非线性优化模型，以确定最优计划设计。将所提出的 AMRGSP 与近期的抽样计划进行性能比较后发现，所提出的计划具有可靠的批次判别能力，并大大减少了检测所需的样本量，具有极佳的成本效益。最后，我们通过实际案例研究对建议的计划进行了评估，以证明其在实践中的适用性。

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引用次数: 0

Unsupervised health indicator construction by a new Gaussian-student’s t-distribution mixture model and its application 利用新型高斯-学生 t 分布混合模型构建无监督健康指标及其应用

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102863

Dingliang Chen , Yi Chai , Yongfang Mao , Yi Qin

The equipment’s remaining useful life (RUL) must be accurately estimated to guarantee its reliable operation. As a crucial part of data-driven RUL prediction, the health indicator (HI) construction method employing the distribution discrepancies can represent the variation trend of health conditions. However, the existing Gaussian mixture model based HI construction method cannot accurately estimate the long-tail distribution characteristics in some degradation data. Moreover, it cannot comprehensively mine the distribution characteristics of degradation data by leveraging different types of distributions. A novel Gaussian-student’s t-distribution mixture model (GSMM) that simultaneously considers Gaussian distribution and student’s t-distribution is developed in this work to estimate the distributions of normal and degradation data. Next, the distribution contact ratio metric (DCRM) is applied to measure the discrepancies between the baseline distribution of normal data and the distributions of test data at different moments. The bearing HI can be constructed with the acquired DCRMs. Finally, the effectiveness and merit of the developed HI construction approach are validated by two bearing life-cycle datasets. The experimental results illustrate that the GSMM-based HI performs better than other classical and state-of-the-art HIs. Additionally, the constructed HI is more suitable for bearing RUL prediction.

必须准确估算设备的剩余使用寿命（RUL），才能保证其可靠运行。作为数据驱动的剩余使用寿命预测的重要组成部分，利用分布差异构建健康指标（HI）的方法可以代表健康状况的变化趋势。然而，现有的基于高斯混合模型的健康指标构建方法无法准确估计某些退化数据的长尾分布特征。此外，它也无法利用不同类型的分布来全面挖掘退化数据的分布特征。本研究开发了一种新颖的高斯分布-学生 t 分布混合模型（GSMM），同时考虑了高斯分布和学生 t 分布，以估计正态和降解数据的分布。然后，应用分布接触比度量（DCRM）来测量正常数据的基线分布与测试数据在不同时刻的分布之间的差异。利用获得的 DCRM 可以构建轴承 HI。最后，两个轴承生命周期数据集验证了所开发的 HI 构建方法的有效性和优点。实验结果表明，基于 GSMM 的 HI 比其他经典和最先进的 HI 性能更好。此外，构建的 HI 更适合轴承 RUL 预测。

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引用次数: 0

Identification of flange specification in real industrial settings with human reasoning assisted by augmented reality 利用增强现实技术辅助人类推理，识别实际工业环境中的法兰规格

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102882

Chih-Hsing Chu, Yen-Ru Chen, Shau-Min Chen

Flange is an important component that connects pressure pipes in livelihood and industrial facilities. The specification information for a flange installed a long time ago is often unavailable or was never recorded. Precision measurement for determining the specification requires detaching the flange from connecting pipes, thus interrupting the facility’s operation. Computer vision techniques cannot guarantee reliable results for mounted flanges that are partially occluded or damaged in real environments. This study develops a new solution, augmented reality (AR)-based recognizer of flange specification (ARFS), to overcome these difficulties. This solution combines human spatial reasoning and computational intelligence in AR to distinguish multiple flanges of similar sizes in complex and uncertain on-site situations. We conducted evaluation experiments to design user interfaces for measuring key flange dimensions in AR. A usability test compares the measuring time and accuracy of the solution implemented on a handheld versus a head-mounted display device. Real-world testing confirms that deploying ARFS as a smartphone app provides an economic yet effective tool for smart asset management in the construction and infrastructure industries.

法兰是连接生活和工业设施中压力管道的重要部件。很久以前安装的法兰的规格信息往往无法获得或从未记录。为确定规格而进行的精确测量需要将法兰从连接管道上拆下，从而中断设施的运行。计算机视觉技术无法保证在实际环境中对部分遮挡或损坏的已安装法兰得出可靠的结果。本研究开发了一种新的解决方案，即基于增强现实（AR）的法兰规格识别器（ARFS），以克服这些困难。该解决方案结合了 AR 中的人类空间推理和计算智能，可在复杂和不确定的现场环境中分辨出多个尺寸相似的法兰。我们进行了评估实验，以设计在 AR 中测量关键法兰尺寸的用户界面。可用性测试比较了在手持设备和头戴显示设备上实施的解决方案的测量时间和准确性。实际测试证实，将 ARFS 作为智能手机应用程序部署，可为建筑和基础设施行业的智能资产管理提供经济而有效的工具。

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引用次数: 0

A survey of autonomous driving frameworks and simulators 自动驾驶框架和模拟器调查

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102850

Hui Zhao , Min Meng , Xiuxian Li , Jia Xu , Li Li , Stephane Galland

Since autonomous driving (AD) is one of the most critical problems in the automobile industry, it has garnered the interest of many academics in recent years. An AD framework or a simulator is used to simulate autonomous vehicles (AVs), it can test modules that an AV needs. There are a large number of researchers studying specific AD simulation tasks, but few studies of systematic AD frameworks and simulators exist. This study distinguishes the functions of AD frameworks and simulators, and it makes a deep study of them, helping promote AV development for researchers, enterprises, and developers. This paper reviews open-source and commercial AD frameworks and simulators, introducing and comparing their features, functionalities, and so on. Additionally, we analyze current research on open-source AD frameworks and simulators according to different applied algorithms and hardware studies and discuss efforts to improve their simulation performance. In the last part, this paper proposes promising research fronts for AD frameworks and simulators for the near future from the viewpoints of hardware deficiencies, AD algorithms, scenario generation, vehicle-to-everything, safety and performance, and co-simulation.

由于自动驾驶（AD）是汽车行业最关键的问题之一，近年来引起了许多学者的兴趣。自动驾驶框架或模拟器用于模拟自动驾驶汽车（AV），它可以测试自动驾驶汽车所需的模块。有大量研究人员在研究具体的自动驾驶汽车模拟任务，但很少有人研究系统的自动驾驶汽车框架和模拟器。本研究区分了自动驾驶汽车框架和模拟器的功能，并对其进行了深入研究，有助于促进研究人员、企业和开发人员的自动驾驶汽车开发。本文综述了开源和商业 AD 框架与模拟器，介绍并比较了它们的特点、功能等。此外，我们还根据不同的应用算法和硬件研究，分析了当前对开源 AD 框架和模拟器的研究，并讨论了为提高其模拟性能所做的努力。最后，本文从硬件缺陷、AD 算法、场景生成、车对万物、安全和性能以及协同仿真等角度，提出了在不久的将来有前景的 AD 框架和模拟器研究方向。

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引用次数: 0

Deciphering laser shock peening quality monitoring: Wavelet-driven network with interpretability 解密激光冲击强化质量监测：具有可解释性的小波驱动网络

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102917

Rui Qin , Zhifen Zhang , Jing Huang , Zhengyao Du , Xizhang Chen , Yu Su , Guangrui Wen , Weifeng He , Xuefeng Chen

Quality monitoring of laser shock peening based on acoustic emission technology is a topical multidisciplinary issue that has received much attention in recent years. To acquire complex time-varying information in acoustic emission signals, convolutional neural networks with powerful learning capabilities have shown potential for a wide range of applications. However, the black-box property of the network imposes limitations on its further development and decision credibility. Therefore, this study proposes a wavelet-driven network with theoretical basis and physical significance. This network can cyclically utilize discrete wavelet packet transform to map input features to the wavelet domain during the learning process, thereby obtaining more robust and valuable information. This paper also constructs a novel wavelet attention mechanism that takes into account the difference between low-frequency and high-frequency information, and is able to allocate resources in both the decomposition component and the time-domain dimension. The proposed method can be seen as a multiresolution analysis technique that combines existing physical knowledge with nonlinear feature processing and feature selective enhancement. The results of the two laser shock peening cases show that the proposed method not only outperforms current state-of-the-art models in terms of monitoring performance, but also has better physical interpretability. Importantly, the proposed method has the potential to be further extended to other interpretable structural health monitoring.

基于声发射技术的激光冲击强化质量监测是近年来备受关注的多学科热点问题。为获取声发射信号中复杂的时变信息，具有强大学习能力的卷积神经网络已显示出广泛的应用潜力。然而，卷积神经网络的黑箱特性限制了其进一步发展和决策可信度。因此，本研究提出了一种具有理论基础和物理意义的小波驱动网络。该网络可在学习过程中循环利用离散小波包变换将输入特征映射到小波域，从而获得更稳健、更有价值的信息。本文还构建了一种新颖的小波关注机制，该机制考虑了低频和高频信息之间的差异，能够在分解分量和时域维度上分配资源。所提出的方法可以看作是一种多分辨率分析技术，它将现有的物理知识与非线性特征处理和特征选择增强相结合。两个激光冲击强化案例的结果表明，所提出的方法不仅在监测性能方面优于目前最先进的模型，而且具有更好的物理可解释性。重要的是，所提出的方法具有进一步扩展到其他可解释结构健康监测的潜力。

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引用次数: 0

Entity alignment method for aeronautical metrology domain based on multi-perspective entity embedding 基于多视角实体嵌入的航空计量领域实体对齐方法

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102908

Shengjie Kong , Xiang Huang , Shuanggao Li , Gen Li , Dong Zhang

The accuracy and consistency of metrology data are the cornerstones of the safety and reliability of aircraft throughout aeronautical products’ lifecycles. Due to the heterogeneous nature of metrology data derived from various sources, knowledge silos commonly emerge, complicating the integration and reuse of knowledge. This study introduces an entity alignment model leveraging multi-perspective embedding. It employs a multi-scale graph convolutional network enhanced by a gating mechanism that aggregates multi-hop neighborhood features to capture the structural embeddings of nodes. Additionally, the model utilizes TransD for representing complex relationships and BERT for capturing entity attributes, facilitating more comprehensive entity representations. Entity alignment is then accomplished by integrating structural, relational, and attribute embeddings using a weighted strategy. In this study, we conducted experimental validation on aeronautical metrology data and also assessed our proposed model on five benchmark datasets. The results indicate that our model significantly outperforms comparative models, demonstrating its potential to enhance the management and application of aeronautical metrology data.

在航空产品的整个生命周期中，计量数据的准确性和一致性是飞机安全性和可靠性的基石。由于来自不同来源的计量数据具有异质性，因此通常会出现知识孤岛，使知识的集成和重用变得更加复杂。本研究介绍了一种利用多视角嵌入的实体对齐模型。它采用了多尺度图卷积网络，并通过门控机制进行了增强，该机制可聚合多跳邻域特征，以捕捉节点的结构嵌入。此外，该模型还利用 TransD 来表示复杂的关系，利用 BERT 来捕捉实体属性，从而促进更全面的实体表示。然后，通过使用加权策略整合结构嵌入、关系嵌入和属性嵌入来完成实体对齐。在这项研究中，我们在航空计量数据上进行了实验验证，并在五个基准数据集上评估了我们提出的模型。结果表明，我们的模型明显优于比较模型，证明了它在加强航空计量数据管理和应用方面的潜力。

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引用次数: 0

Segmentation-aware prior assisted joint global information aggregated 3D building reconstruction 分割感知先验辅助联合全局信息聚合三维建筑重建

IF 8 1区工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Advanced Engineering Informatics

Pub Date : 2024-10-01 DOI: 10.1016/j.aei.2024.102904

Hongxin Peng , Yongjian Liao , Weijun Li , Chuanyu Fu , Guoxin Zhang , Ziquan Ding , Zijie Huang , Qiku Cao , Shuting Cai

Multi-View Stereo plays a pivotal role in civil engineering by facilitating 3D modeling, precise engineering surveying, quantitative analysis, as well as monitoring and maintenance. It serves as a valuable tool, offering high-precision and real-time spatial information crucial for various engineering projects. However, Multi-View Stereo algorithms encounter challenges in reconstructing weakly-textured regions within large-scale building scenes. In these areas, the stereo matching of pixels often fails, leading to inaccurate depth estimations. Based on the Segment Anything Model and RANSAC algorithm, we propose an algorithm that accurately segments weakly-textured regions and constructs their plane priors. These plane priors, combined with triangulation priors, form a reliable prior candidate set. Additionally, we introduce a novel global information aggregation cost function. This function selects optimal plane prior information based on global information in the prior candidate set, constrained by geometric consistency during the depth estimation update process. Experimental results on both the ETH3D benchmark dataset, aerial dataset, building dataset and real scenarios substantiate the superior performance of our method in producing 3D building models compared to other state-of-the-art methods. In summary, our work aims to enhance the completeness and density of 3D building reconstruction, carrying implications for broader applications in urban planning and virtual reality.

Multi-View Stereo 在土木工程中发挥着举足轻重的作用，有助于三维建模、精确工程测量、定量分析以及监测和维护。它是一种宝贵的工具，可提供对各种工程项目至关重要的高精度实时空间信息。然而，多视图立体算法在重建大规模建筑场景中的弱纹理区域时遇到了挑战。在这些区域，像素的立体匹配经常失败，导致深度估计不准确。我们基于 "分割任意模型 "和 RANSAC 算法，提出了一种能准确分割弱纹理区域并构建其平面先验的算法。这些平面先验与三角测量先验相结合，形成了可靠的先验候选集。此外，我们还引入了一种新颖的全局信息聚合成本函数。在深度估计更新过程中，该函数根据先验候选集中的全局信息选择最优的平面先验信息，并受到几何一致性的限制。在 ETH3D 基准数据集、航拍数据集、建筑数据集和真实场景上的实验结果证明，与其他最先进的方法相比，我们的方法在生成三维建筑模型方面表现出色。总之，我们的工作旨在提高三维建筑重建的完整性和密度，为城市规划和虚拟现实领域的更广泛应用带来影响。

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引用次数: 0