Graphical Models最新文献

英文中文

Design method of porous scaffold based on TPMS and KM growth function 基于TPMS和KM生长函数的多孔支架设计方法

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-05-02 DOI: 10.1016/j.gmod.2025.101265

Yi Zhang, Jiong Liu, Quan Qi

Additive manufacturing technology, as a cutting-edge manufacturing process, has shown great potential in the preparation of 3D tissue engineering scaffolds with complex pore structures. The multi-functional scaffolds produced by this technology can provide a combination of excellent mechanical and biological properties, such as high stiffness, strength, toughness and good fluid permeability. Among many stent design methods, pore shape design based on triply periodic minimal surface is favored because of its advantages in controlling design parameters such as aperture, pore shape and internal channel connectivity. In this paper, a new design method for multi-shape scaffolds is proposed, which combines KM growth function and shape conformal mixing technique to construct a complex pore structure consisting of multiple triply periodic minimal surfaces and arbitrary transition boundaries in a scaffold. The advantage of this method is that it can flexibly control the mixed form of the resulting scaffold, and then explore and optimize a variety of transition pore forms to achieve a combination of multi-functiona characteristics such as high strength and excellent fluid permeability.

增材制造技术作为一种前沿制造工艺，在制备具有复杂孔隙结构的三维组织工程支架方面显示出巨大的潜力。该技术生产的多功能支架具有较高的刚度、强度、韧性和良好的透气性等优良的机械性能和生物性能。在众多支架设计方法中，基于三周期最小表面的孔形设计因其在孔径、孔形和内部通道连通性等设计参数控制方面的优势而受到青睐。本文提出了一种新的多形状支架设计方法，该方法将KM生长函数与形状保形混合技术相结合，在支架中构建由多个三周期极小面和任意过渡边界组成的复杂孔隙结构。该方法的优点是可以灵活控制所得支架的混合形态，进而探索和优化多种过渡孔隙形态，实现高强度和优异的流体渗透性等多功能特性的结合。

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引用次数: 0

SEDFMNet: A Simple and Efficient Unsupervised Functional Map for Shape Correspondence Based on Deconstruction SEDFMNet：一种简单高效的基于解构的形状对应的无监督函数映射

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-06-04 DOI: 10.1016/j.gmod.2025.101270

Haojun Xu , Qinsong Li , Ling Hu , Shengjun Liu , Haibo Wang , Xinru Liu

In recent years, deep functional maps (DFM) have emerged as a leading learning-based framework for non-rigid shape-matching problems, offering diverse network architectures for this domain. This richness also makes exploring better and novel design beliefs for existing powerful DFM components to promote performance meaningful and engaging. This paper delves into this problem and successfully produces the SEDFMNet, a simple yet highly efficient DFM pipeline. To achieve this, we systematically deconstruct the core modules of the general DFM framework and analyze key design choices in existing approaches to identify the most critical components through extensive experiments. By reassembling these crucial components, we culminate in developing our SEDFMNet, which features a simpler structure than conventional DFM pipelines while delivering superior performance. Our approach is rigorously validated through comprehensive experiments on diverse datasets, where the SEDFMNet consistently achieves state-of-the-art results, even in challenging scenarios such as non-isometric shape matching and shape matching with topological noise. Our work offers fresh insights into DFM research and opens new avenues for advancing this field.

近年来，深度功能图（DFM）已成为非刚性形状匹配问题的主要基于学习的框架，为该领域提供了多种网络架构。这种丰富性还有助于为现有功能强大的DFM组件探索更好和新颖的设计信念，以提高性能的意义和吸引力。本文对这一问题进行了深入的研究，并成功地实现了一种简单而高效的DFM流水线SEDFMNet。为了实现这一目标，我们系统地解构了通用DFM框架的核心模块，并通过广泛的实验分析了现有方法中的关键设计选择，以确定最关键的组件。通过重新组装这些关键组件，我们最终开发出了SEDFMNet，它的结构比传统的DFM管道更简单，同时提供了卓越的性能。我们的方法通过在不同数据集上的综合实验得到了严格的验证，即使在非等距形状匹配和带有拓扑噪声的形状匹配等具有挑战性的场景中，SEDFMNet也始终能够获得最先进的结果。我们的工作为DFM研究提供了新的见解，并为推进这一领域开辟了新的途径。

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引用次数: 0

Human perception faithful curve reconstruction based on persistent homology and principal curve 基于持久同调和主曲线的人类感知忠实曲线重建

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-05-24 DOI: 10.1016/j.gmod.2025.101267

Yu Chen, Hongwei Lin, Yifan Xing

Reconstructing curves that align with human visual perception from a noisy point cloud presents a significant challenge in the field of curve reconstruction. A specific problem involves reconstructing curves from a noisy point cloud sampled from multiple intersecting curves, ensuring that the reconstructed results align with the Gestalt principles and thus produce curves faithful to human perception. This task involves identifying all potential curves from a point cloud and reconstructing approximating curves, which is critical in applications such as trajectory reconstruction, path planning, and computer vision. In this study, we propose an automatic method that utilizes the topological understanding provided by persistent homology and the local principal curve method to separate and approximate the intersecting closed curves from point clouds, ultimately achieving successful human perception faithful curve reconstruction results using B-spline curves. This technique effectively addresses noisy data clouds and intersections, as demonstrated by experimental results.

从噪声点云中重建符合人类视觉感知的曲线是曲线重建领域的一个重大挑战。一个具体的问题涉及从多个相交曲线采样的噪声点云重建曲线，确保重建结果符合格式塔原则，从而产生忠实于人类感知的曲线。该任务包括从点云中识别所有潜在曲线并重建近似曲线，这在轨迹重建、路径规划和计算机视觉等应用中至关重要。在本研究中，我们提出了一种自动方法，利用持久同调提供的拓扑理解和局部主曲线方法从点云中分离和近似相交的封闭曲线，最终获得成功的人类感知忠实的b样条曲线重建结果。实验结果表明，该技术有效地解决了噪声数据云和交叉的问题。

引用次数: 0

RMAvatar: Photorealistic human avatar reconstruction from monocular video based on rectified mesh-embedded Gaussians RMAvatar：基于校正网格嵌入高斯函数的单目视频真人头像重建

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-05-24 DOI: 10.1016/j.gmod.2025.101266

Sen Peng , Weixing Xie , Zilong Wang , Xiaohu Guo , Zhonggui Chen , Baorong Yang , Xiao Dong

We introduce RMAvatar, a novel human avatar representation with Gaussian splatting embedded on mesh to learn clothed avatar from a monocular video. We utilize the explicit mesh geometry to represent motion and shape of a virtual human and implicit appearance rendering with Gaussian Splatting. Our method consists of two main modules: Gaussian initialization module and Gaussian rectification module. We embed Gaussians into triangular faces and control their motion through the mesh, which ensures low-frequency motion and surface deformation of the avatar. Due to the limitations of LBS formula, the human skeleton is hard to control complex non-rigid transformations. We then design a pose-related Gaussian rectification module to learn fine-detailed non-rigid deformations, further improving the realism and expressiveness of the avatar. We conduct extensive experiments on public datasets, and RMAvatar shows state-of-the-art performance on both rendering quality and quantitative evaluations. Please see our project page at https://rm-avatar.github.io.

我们引入了一种新的基于网格的高斯溅射的人类化身表示——RMAvatar来学习单目视频中的穿着的化身。我们利用显式网格几何来表示虚拟人的运动和形状，并使用高斯飞溅来隐式渲染外观。该方法包括两个主要模块：高斯初始化模块和高斯校正模块。我们将高斯函数嵌入到三角形面中，并通过网格控制它们的运动，从而保证了角色的低频运动和表面变形。由于LBS公式的限制，人体骨架难以控制复杂的非刚性变换。然后，我们设计了一个与姿态相关的高斯校正模块来学习精细的非刚性变形，进一步提高化身的真实感和表现力。我们在公共数据集上进行了大量的实验，RMAvatar在渲染质量和定量评估上都显示了最先进的性能。请参阅我们的项目页面https://rm-avatar.github.io。

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引用次数: 0

Computing medial axis of a simple polygon in linear time based on R-L sequence 基于R-L序列在线性时间内计算简单多边形的中轴线

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-03-24 DOI: 10.1016/j.gmod.2025.101258

Hongyu Chen, Xiaodiao Chen, Yizhao Xue

Medial axis computation has wide applications in pattern recognition, image processing, finite element mesh generation, and CNC tool path extraction. Aiming to explore intrinsic geometric attributes of the medial axis of a simple polygon which can be accurately represented and faces its challenge of computational efficiency, an R-L sequence-based algorithm of linear computational complexity is proposed for achieving much higher efficiency; especially, it enables the complexity of Delaunay triangulation to be linear. The algorithm is done by reconstructing the Voronoi diagram tree of the given simple polygon, which can be easily performed in a breadth-first manner with a higher computational efficiency. The branches of the medial axis are naturally divided into several panels, such that the branches in the same panel cause no interference with each other and decrease a lot of computational costs. Based on our experiments, the efficiency of the proposed R-L algorithm can be 6 to 17 times greater than that of the state-of-the-art method in TVCG, and up to 419 times greater than the CGAL algorithm. In principle, it can be directly applied to compute the medial axis of curvilinear polygons, which expands the scope of application compared to Chin’s method.

中轴计算在模式识别、图像处理、有限元网格生成和数控刀具路径提取中有着广泛的应用。为了探索可精确表示的简单多边形中轴线的内在几何属性，面对其计算效率的挑战，提出了一种基于 R-L 序列的线性计算复杂度算法，以实现更高的效率，特别是使 Delaunay 三角剖分的复杂度成为线性。该算法通过重建给定简单多边形的 Voronoi 图树来完成，可以轻松地以广度优先的方式执行，具有更高的计算效率。中轴的分支被自然地划分为多个板块，这样同一板块中的分支就不会相互干扰，从而降低了大量的计算成本。根据我们的实验，所提出的 R-L 算法的效率是 TVCG 中最先进方法的 6 到 17 倍，是 CGAL 算法的 419 倍。原则上，它可以直接用于计算曲线多边形的中轴线，与 Chin 的方法相比扩大了应用范围。

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引用次数: 0

FastClothGNN: Optimizing message passing in Graph Neural Networks for accelerating real-time cloth simulation FastClothGNN：优化图神经网络中的消息传递，加速实时布料模拟

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-06-08 DOI: 10.1016/j.gmod.2025.101273

Yang Zhang, Kailuo Yu, Xinyu Zhang

We present an efficient message aggregation algorithm FastClothGNN for Graph Neural Networks (GNNs) specifically designed for real-time cloth simulation in virtual try-on systems. Our approach reduces computational redundancy by optimizing neighbor sampling and minimizing unnecessary message-passing between cloth and obstacle nodes. This significantly accelerates the real-time performance of cloth simulation, making it ideal for interactive virtual environments. Our experiments demonstrate that our algorithm significantly enhances memory efficiency and improve the performance both in training and in inference in GNNs. This optimization enables our algorithm to be effectively applied to resource-constrained, providing users with more seamless and immersive interactions and thereby increasing the potential for practical real-time applications.

我们提出了一种高效的消息聚合算法FastClothGNN，用于专门设计用于虚拟试穿系统中实时布料模拟的图神经网络（gnn）。我们的方法通过优化邻居采样和最小化布节点和障碍节点之间不必要的消息传递来减少计算冗余。这大大加快了布料模拟的实时性能，使其成为交互式虚拟环境的理想选择。实验表明，该算法显著提高了gnn的记忆效率，提高了训练和推理的性能。这种优化使我们的算法能够有效地应用于资源受限的环境中，为用户提供更加无缝和沉浸式的交互，从而增加了实际实时应用的潜力。

引用次数: 0

Topology-controlled Laplace–Beltrami operator on point clouds based on persistent homology 基于持久同调的点云拓扑控制拉普拉斯-贝尔特拉米算子

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-04-02 DOI: 10.1016/j.gmod.2025.101261

Ao Zhang, Qing Fang, Peng Zhou, Xiao-Ming Fu

Computing the Laplace–Beltrami operator on point clouds is essential for tasks such as smoothing and shape analysis. Unlike meshes, determining the Laplace–Beltrami operator on point clouds requires establishing neighbors for each point. However, traditional

k

-nearest neighbors (k-NN) methods for estimating local neighborhoods often introduce spurious connectivities that distort the manifold topology. We propose a novel approach that leverages persistent homology to refine the neighborhood graph by identifying and removing erroneous edges. Starting with an initial k-NN graph, we assign weights based on local tangent plane estimations and construct a Vietoris–Rips complex. Persistent homology is then employed to detect and eliminate spurious edges through a topological optimization process. This iterative refinement results in a more accurate neighborhood graph that better represents the underlying manifold, enabling precise discretization of the Laplace–Beltrami operator. Experimental results on various point cloud datasets demonstrate that our method outperforms traditional k-NN approaches by more accurately capturing the manifold topology and enhancing downstream computations such as spectral analysis.

计算点云上的拉普拉斯-贝尔特拉米算子对于平滑和形状分析等任务至关重要。与网格不同，确定点云上的拉普拉斯-贝尔特拉米算子需要为每个点建立邻居。然而，传统的k近邻（k-NN）估计局部邻域的方法经常引入扭曲流形拓扑的虚假连通性。我们提出了一种新的方法，利用持久同源性来改进邻域图，通过识别和去除错误边。从初始k-NN图开始，我们根据局部切平面估计分配权重，并构造一个Vietoris-Rips复合体。然后，通过拓扑优化过程，利用持久同源性来检测和消除假边。这种迭代的细化会产生更精确的邻域图，更好地表示底层流形，从而实现拉普拉斯-贝尔特拉米算子的精确离散化。在各种点云数据集上的实验结果表明，我们的方法通过更准确地捕获流形拓扑和增强下游计算（如频谱分析）来优于传统的k-NN方法。

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引用次数: 0

An efficient parametric modeling and path planning method for 3D printing of curved surface corrugated sandwich structures 曲面波纹夹层结构三维打印的高效参数化建模与路径规划方法

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-04-10 DOI: 10.1016/j.gmod.2025.101263

Tan Gui , Zhihong Li , Yongjun Cao , Jianghong Yang , Yingjun Wang

This study proposes an efficient preprocessing method and parametric modeling technique for the path planning of corrugated curved surface sandwich structures. Focusing on the characteristics of Fused Deposition Modeling (FDM), the model undergoes preprocessing for two types of path planning, segmenting the sandwich structure for Eulerian Path Printing (EPP) and Eulerian Circuit Printing (ECP). Algorithms were developed using the SolidWorks API for secondary development, resulting in a standalone plugin module. This plugin streamlines adaptive modeling of corrugated sandwich structures on curved surfaces, showcasing strong versatility. Additionally, a comparison of the printing time between preprocessed models and standard models reveals a significant reduction in nozzle idle time. Moreover, as the infill density increases, the reduction in printing time becomes more pronounced. Finally, compression tests confirmed that printed parts obtained using the EPP and ECP methods maintained comparable mechanical properties to those printed using conventional methods.

提出了一种有效的波纹曲面夹层结构路径规划预处理方法和参数化建模技术。针对熔融沉积建模（FDM）的特点，对模型进行两种路径规划的预处理，分割出欧拉路径打印（EPP）和欧拉电路打印（ECP）的夹层结构。算法使用SolidWorks API进行二次开发，从而形成一个独立的插件模块。这个插件简化了波纹夹层结构在曲面上的自适应建模，展示了强大的多功能性。此外，对预处理模型和标准模型之间的打印时间进行比较，发现喷嘴空闲时间显着减少。此外，随着填充密度的增加，印刷时间的减少变得更加明显。最后，压缩测试证实，使用EPP和ECP方法获得的打印部件与使用常规方法打印的部件保持相当的机械性能。

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引用次数: 0

3DMeshNet: A three-dimensional differential neural network for structured mesh generation 3DMeshNet：用于结构化网格生成的三维微分神经网络

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-03-25 DOI: 10.1016/j.gmod.2025.101257

Jiaming Peng, Xinhai Chen, Jie Liu

Mesh generation is a crucial step in numerical simulations, significantly impacting simulation accuracy and efficiency. However, generating meshes remains time-consuming and requires expensive computational resources. In this paper, we propose a novel method, 3DMeshNet, for three-dimensional structured mesh generation. The method embeds the meshing-related differential equations into the loss function of neural networks, formulating the meshing task as an unsupervised optimization problem. It takes geometric points as input to learn the potential mapping between parametric and computational spaces. After suitable offline training, 3DMeshNet can efficiently output a three-dimensional structured mesh with a user-defined number of quadrilateral/hexahedral cells through the feed-forward neural prediction. To enhance training stability and accelerate convergence, we integrate loss function reweighting through weight adjustments and gradient projection alongside applying finite difference methods to streamline derivative computations in the loss. Experiments on different cases show that 3DMeshNet is robust and fast. It outperforms neural network-based methods and yields superior meshes compared to traditional mesh partitioning methods. 3DMeshNet significantly reduces training times by up to 85% compared to other neural network-based approaches and lowers meshing overhead by 4 to 8 times relative to traditional meshing methods.

网格生成是数值仿真的关键步骤，对仿真精度和效率有重要影响。然而，生成网格仍然耗时且需要昂贵的计算资源。本文提出了一种新的三维结构网格生成方法3DMeshNet。该方法将网格划分相关的微分方程嵌入到神经网络的损失函数中，将网格划分任务转化为无监督优化问题。它以几何点作为输入来学习参数空间和计算空间之间的潜在映射。3DMeshNet经过适当的离线训练后，可以通过前馈神经预测高效地输出具有自定义数量的四边形/六面体单元的三维结构化网格。为了增强训练稳定性和加速收敛，我们通过权值调整和梯度投影来整合损失函数重加权，同时应用有限差分方法来简化损失中的导数计算。不同实例的实验表明，3DMeshNet具有鲁棒性和快速性。与传统的网格划分方法相比，它优于基于神经网络的方法，并产生更好的网格。与其他基于神经网络的方法相比，3DMeshNet显著减少了高达85%的训练时间，与传统的网格划分方法相比，将网格划分开销降低了4到8倍。

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引用次数: 0

Additive manufacturing toward 2D and 3D freeform lattices with conforming compliant bi-/multi-stable tiles 二维和三维自由网格的增材制造，具有符合要求的双/多稳定瓷砖

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models

Pub Date : 2025-06-01 Epub Date: 2025-03-28 DOI: 10.1016/j.gmod.2025.101260

Gershon Elber

Compliant mechanisms have captured the attention of many researchers in recent years, with ever-expanding mechanical and physical behaviors. Moreover, the fabrication of such mechanisms has been greatly simplified with the enabling technology of 3D printing.

Drawing from existing lattice construction abilities, in this work we explore the abilities of constructing whole freeform lattices, where the tiles in these lattices are compliant mechanisms, possibly heterogeneous. Specifically, herein we focus on bi-stable and multi-stable tiles, or tiles with two or more mechanical stable states. The introduced approach will be exemplified on a variety of 2D and 3D lattices, fabricated with the aid of additive manufacturing.

近年来，柔性机构的力学和物理行为不断扩大，引起了许多研究人员的关注。此外，这种机构的制造已经大大简化与使能技术的3D打印。从现有的晶格构建能力，在这项工作中，我们探索构建整个自由形式晶格的能力，其中这些晶格中的瓦片是柔性机制，可能是异质的。具体来说，这里我们关注双稳定和多稳定瓷砖，或者具有两个或更多机械稳定状态的瓷砖。所介绍的方法将在各种2D和3D晶格上进行举例说明，这些晶格是借助增材制造制造的。

引用次数: 0

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