Pub Date : 2025-05-24DOI: 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.
{"title":"Human perception faithful curve reconstruction based on persistent homology and principal curve","authors":"Yu Chen, Hongwei Lin, Yifan Xing","doi":"10.1016/j.gmod.2025.101267","DOIUrl":"10.1016/j.gmod.2025.101267","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101267"},"PeriodicalIF":2.5,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-24DOI: 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.
{"title":"RMAvatar: Photorealistic human avatar reconstruction from monocular video based on rectified mesh-embedded Gaussians","authors":"Sen Peng , Weixing Xie , Zilong Wang , Xiaohu Guo , Zhonggui Chen , Baorong Yang , Xiao Dong","doi":"10.1016/j.gmod.2025.101266","DOIUrl":"10.1016/j.gmod.2025.101266","url":null,"abstract":"<div><div>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 <span><span>https://rm-avatar.github.io</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101266"},"PeriodicalIF":2.5,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-23DOI: 10.1016/j.gmod.2025.101268
Zi-An Wang , Shihao Zou , Shiyao Yu , Mingyuan Zhang , Chao Dong
Recent advances in interactive technologies have highlighted the prominence of audio signals for semantic encoding. This paper explores a new task, where audio signals are used as conditioning inputs to generate motions that align with the semantics of the audio. Unlike text-based interactions, audio provides a more natural and intuitive communication method. However, existing methods typically focus on matching motions with music or speech rhythms, which often results in a weak connection between the semantics of the audio and generated motions. We propose an end-to-end framework using a masked generative transformer, enhanced by a memory-retrieval attention module to handle sparse and lengthy audio inputs. Additionally, we enrich existing datasets by converting descriptions into conversational style and generating corresponding audio with varied speaker identities. Experiments demonstrate the effectiveness and efficiency of the proposed framework, demonstrating that audio instructions can convey semantics similar to text while providing more practical and user-friendly interactions.
{"title":"Semantics-aware human motion generation from audio instructions","authors":"Zi-An Wang , Shihao Zou , Shiyao Yu , Mingyuan Zhang , Chao Dong","doi":"10.1016/j.gmod.2025.101268","DOIUrl":"10.1016/j.gmod.2025.101268","url":null,"abstract":"<div><div>Recent advances in interactive technologies have highlighted the prominence of audio signals for semantic encoding. This paper explores a new task, where audio signals are used as conditioning inputs to generate motions that align with the semantics of the audio. Unlike text-based interactions, audio provides a more natural and intuitive communication method. However, existing methods typically focus on matching motions with music or speech rhythms, which often results in a weak connection between the semantics of the audio and generated motions. We propose an end-to-end framework using a masked generative transformer, enhanced by a memory-retrieval attention module to handle sparse and lengthy audio inputs. Additionally, we enrich existing datasets by converting descriptions into conversational style and generating corresponding audio with varied speaker identities. Experiments demonstrate the effectiveness and efficiency of the proposed framework, demonstrating that audio instructions can convey semantics similar to text while providing more practical and user-friendly interactions.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101268"},"PeriodicalIF":2.5,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-02DOI: 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.
{"title":"Design method of porous scaffold based on TPMS and KM growth function","authors":"Yi Zhang, Jiong Liu, Quan Qi","doi":"10.1016/j.gmod.2025.101265","DOIUrl":"10.1016/j.gmod.2025.101265","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101265"},"PeriodicalIF":2.5,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-24DOI: 10.1016/j.gmod.2025.101264
Pengbo Bo , Siyu Xue , Xiwen Xu , Caiming Zhang
Tool shape selection and path planning are critical for 5-axis CNC flank milling of freeform surfaces, typically addressed using optimization algorithms where initialization plays a pivotal role. Existing approaches rely on user-specified initialization of either tool shapes or motion paths, often resulting in suboptimal outcomes. This paper introduces a fully automated method that simultaneously initializes both tool shapes and motion paths, achieving high-precision machining with efficient surface coverage. Our approach explores a solution space of potential tool axes represented by line segments near the design surface. To efficiently manage the vast number of lines, we integrate space voxelization with a discrete distance field for effective line sampling. A graph-based algorithm generates feasible line sequences for motion paths, while path optimization refines a single tool shape across multiple paths simultaneously. The method identifies optimal tool shapes of various sizes, each paired with corresponding motion paths for multi-pass machining. Experiments on industrial benchmark models and freeform surfaces validate the effectiveness and practicality of the proposed approach.
{"title":"Initialization of cutting tools and milling paths for 5-axis CNC flank milling of freeform surfaces","authors":"Pengbo Bo , Siyu Xue , Xiwen Xu , Caiming Zhang","doi":"10.1016/j.gmod.2025.101264","DOIUrl":"10.1016/j.gmod.2025.101264","url":null,"abstract":"<div><div>Tool shape selection and path planning are critical for 5-axis CNC flank milling of freeform surfaces, typically addressed using optimization algorithms where initialization plays a pivotal role. Existing approaches rely on user-specified initialization of either tool shapes or motion paths, often resulting in suboptimal outcomes. This paper introduces a fully automated method that simultaneously initializes both tool shapes and motion paths, achieving high-precision machining with efficient surface coverage. Our approach explores a solution space of potential tool axes represented by line segments near the design surface. To efficiently manage the vast number of lines, we integrate space voxelization with a discrete distance field for effective line sampling. A graph-based algorithm generates feasible line sequences for motion paths, while path optimization refines a single tool shape across multiple paths simultaneously. The method identifies optimal tool shapes of various sizes, each paired with corresponding motion paths for multi-pass machining. Experiments on industrial benchmark models and freeform surfaces validate the effectiveness and practicality of the proposed approach.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101264"},"PeriodicalIF":2.5,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-11DOI: 10.1016/j.gmod.2025.101262
Xufei Guo , Xiao Dong , Juan Cao , Zhonggui Chen
The creation of computational agents capable of generating computer-aided design (CAD) models that rival those produced by professional designers is a pressing challenge in the field of computational design. The key obstacle is the need to generate a large number of realistic and diverse models while maintaining control over the output to a certain degree. Therefore, we propose a novel CAD model generation network called CADTrans which is based on a code tree-guided transformer framework to autoregressively generate CAD construction sequences. Firstly, three regularized discrete codebooks are extracted through vector quantized adversarial learning, with each codebook respectively representing the features of Loop, Profile, and Solid. Secondly, these codebooks are used to normalize a CAD construction sequence into a structured code tree representation which is then used to train a standard transformer network to reconstruct the code tree. Finally, the code tree is used as global information to guide the sketch-and-extrude method to recover the corresponding geometric information, thereby reconstructing the complete CAD model. Extensive experiments demonstrate that CADTrans achieves state-of-the-art performance, generating higher-quality, more varied, and complex models. Meanwhile, it provides more possibilities for CAD applications through its flexible control method, enabling users to quickly experiment with different design schemes, inspiring diverse design ideas and the generation of a wide variety of models or even inspiring models, thereby improving design efficiency and promoting creativity. The code is available at https://effieguoxufei.github.io/CADtrans/.
{"title":"CADTrans: A code tree-guided CAD generative transformer model with regularized discrete codebooks","authors":"Xufei Guo , Xiao Dong , Juan Cao , Zhonggui Chen","doi":"10.1016/j.gmod.2025.101262","DOIUrl":"10.1016/j.gmod.2025.101262","url":null,"abstract":"<div><div>The creation of computational agents capable of generating computer-aided design (CAD) models that rival those produced by professional designers is a pressing challenge in the field of computational design. The key obstacle is the need to generate a large number of realistic and diverse models while maintaining control over the output to a certain degree. Therefore, we propose a novel CAD model generation network called CADTrans which is based on a code tree-guided transformer framework to autoregressively generate CAD construction sequences. Firstly, three regularized discrete codebooks are extracted through vector quantized adversarial learning, with each codebook respectively representing the features of Loop, Profile, and Solid. Secondly, these codebooks are used to normalize a CAD construction sequence into a structured code tree representation which is then used to train a standard transformer network to reconstruct the code tree. Finally, the code tree is used as global information to guide the sketch-and-extrude method to recover the corresponding geometric information, thereby reconstructing the complete CAD model. Extensive experiments demonstrate that CADTrans achieves state-of-the-art performance, generating higher-quality, more varied, and complex models. Meanwhile, it provides more possibilities for CAD applications through its flexible control method, enabling users to quickly experiment with different design schemes, inspiring diverse design ideas and the generation of a wide variety of models or even inspiring models, thereby improving design efficiency and promoting creativity. The code is available at <span><span>https://effieguoxufei.github.io/CADtrans/</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101262"},"PeriodicalIF":2.5,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-10DOI: 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.
{"title":"An efficient parametric modeling and path planning method for 3D printing of curved surface corrugated sandwich structures","authors":"Tan Gui , Zhihong Li , Yongjun Cao , Jianghong Yang , Yingjun Wang","doi":"10.1016/j.gmod.2025.101263","DOIUrl":"10.1016/j.gmod.2025.101263","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101263"},"PeriodicalIF":2.5,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 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 -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.
{"title":"Topology-controlled Laplace–Beltrami operator on point clouds based on persistent homology","authors":"Ao Zhang, Qing Fang, Peng Zhou, Xiao-Ming Fu","doi":"10.1016/j.gmod.2025.101261","DOIUrl":"10.1016/j.gmod.2025.101261","url":null,"abstract":"<div><div>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 <span><math><mi>k</mi></math></span>-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.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101261"},"PeriodicalIF":2.5,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-28DOI: 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.
{"title":"Additive manufacturing toward 2D and 3D freeform lattices with conforming compliant bi-/multi-stable tiles","authors":"Gershon Elber","doi":"10.1016/j.gmod.2025.101260","DOIUrl":"10.1016/j.gmod.2025.101260","url":null,"abstract":"<div><div>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.</div><div>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.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101260"},"PeriodicalIF":2.5,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-25DOI: 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.
{"title":"3DMeshNet: A three-dimensional differential neural network for structured mesh generation","authors":"Jiaming Peng, Xinhai Chen, Jie Liu","doi":"10.1016/j.gmod.2025.101257","DOIUrl":"10.1016/j.gmod.2025.101257","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101257"},"PeriodicalIF":2.5,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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