Pub Date : 2026-01-01DOI: 10.1016/j.gmod.2026.101320
K. He, J.B.T.M. Roerdink, J. Kosinka
Vector graphics provide continuous and often even smooth geometric representations of images. While recent approaches to automatically vectorize images lead to relatively good results, they typically leave ample room for improvement: the geometry and color of the vector graphics primitives can be further (automatically) optimized. We propose a novel method that generates high-quality vectorizations based on optimizing input curved triangle meshes (optionally with mesh colors). To overcome the key challenge of establishing a differentiable mapping between the input parameters, i.e., geometry and (mesh) colors of the gradient mesh, and the difference between the vectorized and input image, we treat the input image as a continuous bilinear interpolatory spline and employ Monte Carlo integration. We test our algorithm on various images and show that it can effectively and efficiently improve the quality of an initial vectorization.
{"title":"Monte Carlo optimization for gradient meshes","authors":"K. He, J.B.T.M. Roerdink, J. Kosinka","doi":"10.1016/j.gmod.2026.101320","DOIUrl":"10.1016/j.gmod.2026.101320","url":null,"abstract":"<div><div>Vector graphics provide continuous and often even smooth geometric representations of images. While recent approaches to automatically vectorize images lead to relatively good results, they typically leave ample room for improvement: the geometry and color of the vector graphics primitives can be further (automatically) optimized. We propose a novel method that generates high-quality vectorizations based on optimizing input curved triangle meshes (optionally with mesh colors). To overcome the key challenge of establishing a differentiable mapping between the input parameters, i.e., geometry and (mesh) colors of the gradient mesh, and the difference between the vectorized and input image, we treat the input image as a continuous bilinear interpolatory spline and employ Monte Carlo integration. We test our algorithm on various images and show that it can effectively and efficiently improve the quality of an initial vectorization.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"143 ","pages":"Article 101320"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924846","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 : 2026-01-01DOI: 10.1016/j.gmod.2025.101319
Rengan Xie , Kai Huang , Xiaoliang Luo , Yizheng Chen , Lvchun Wang , Qi Wang , Qi Ye , Wei Chen , Wenting Zheng , Yuchi Huo
{"title":"Corrigendum to “LDM: Large tensorial SDF model for textured mesh generation” [Graphical Models, Volume 140, August 2025, 101271]","authors":"Rengan Xie , Kai Huang , Xiaoliang Luo , Yizheng Chen , Lvchun Wang , Qi Wang , Qi Ye , Wei Chen , Wenting Zheng , Yuchi Huo","doi":"10.1016/j.gmod.2025.101319","DOIUrl":"10.1016/j.gmod.2025.101319","url":null,"abstract":"","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"143 ","pages":"Article 101319"},"PeriodicalIF":2.2,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022960","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 : 2025-11-22DOI: 10.1016/j.gmod.2025.101309
Yixin Xu , Shiguang Liu
Chilling injury (CI) is a major postharvest physiological disorder in fruits, causing quality degradation and economic losses during low-temperature storage. While physically-based methods exist for simulating plant deformation, they are computationally intensive and not optimized for capturing the subtle, spatially distributed symptoms of CI, such as browning, pitting, and wrinkling. In this paper, we propose a biologically-informed, texture-based framework for dynamic CI simulation that links biological symptom progression to visual representation. Browning and pitting are modeled using a texture-based de-chilling technique driven by a Logistic model of the chilling injury index (CII), with a histogram-matching-based algorithm ensuring alignment between simulated symptoms and CII values. Wrinkling is simulated by combining kinetic models of water loss with bump maps generated using Worley noise, which approximate the quasi-random yet locally continuous surface deformations caused by epidermal shrinkage. The proposed framework efficiently integrates biologically-driven modeling, dynamic texture evolution, and water-loss-induced surface deformation, producing realistic CI simulations without high-resolution meshes. It applies to multiple fruit types — including tropical climacteric (banana), Solanaceous (tomato), Cucurbit (cucumber), and Citrus (orange, lemon) — offering an effective approach for visualizing CI progression.
{"title":"Visual simulation of fruit chilling injury","authors":"Yixin Xu , Shiguang Liu","doi":"10.1016/j.gmod.2025.101309","DOIUrl":"10.1016/j.gmod.2025.101309","url":null,"abstract":"<div><div>Chilling injury (CI) is a major postharvest physiological disorder in fruits, causing quality degradation and economic losses during low-temperature storage. While physically-based methods exist for simulating plant deformation, they are computationally intensive and not optimized for capturing the subtle, spatially distributed symptoms of CI, such as browning, pitting, and wrinkling. In this paper, we propose a biologically-informed, texture-based framework for dynamic CI simulation that links biological symptom progression to visual representation. Browning and pitting are modeled using a texture-based de-chilling technique driven by a Logistic model of the chilling injury index (CII), with a histogram-matching-based algorithm ensuring alignment between simulated symptoms and CII values. Wrinkling is simulated by combining kinetic models of water loss with bump maps generated using Worley noise, which approximate the quasi-random yet locally continuous surface deformations caused by epidermal shrinkage. The proposed framework efficiently integrates biologically-driven modeling, dynamic texture evolution, and water-loss-induced surface deformation, producing realistic CI simulations without high-resolution meshes. It applies to multiple fruit types — including tropical climacteric (banana), Solanaceous (tomato), Cucurbit (cucumber), and Citrus (orange, lemon) — offering an effective approach for visualizing CI progression.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"143 ","pages":"Article 101309"},"PeriodicalIF":2.2,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145571882","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 : 2025-11-14DOI: 10.1016/j.gmod.2025.101308
Chiara Sorgentone
Applications in fields such as fluid mechanics, video games and image processing frequently involve the simulation of 3D objects with spherical topology, with a surface quantity that varies with the geometry and/or according to some surface partial differential equation. However, when the geometry undergoes continuous deformations, significant distortions in the surface point distribution may arise. This can lead to aliasing effects and numerical instability, reducing the overall accuracy of the simulation.
To address this issue, we introduce a novel tool to improve the efficiency of a spectral reparametrization procedure able to ensure the optimal representation of such 3D objects and surface quantities, even when dealing with long time simulations. This new strategy makes the reparametrization technique fully adaptive, selecting only the frequencies that are needed to represent a given surface, improving the efficiency of the algorithm, preventing degradation in the quality of the simulation and enhancing the overall stability.
{"title":"Optimal representation of time-dependent spherical geometries","authors":"Chiara Sorgentone","doi":"10.1016/j.gmod.2025.101308","DOIUrl":"10.1016/j.gmod.2025.101308","url":null,"abstract":"<div><div>Applications in fields such as fluid mechanics, video games and image processing frequently involve the simulation of 3D objects with spherical topology, with a surface quantity that varies with the geometry and/or according to some surface partial differential equation. However, when the geometry undergoes continuous deformations, significant distortions in the surface point distribution may arise. This can lead to aliasing effects and numerical instability, reducing the overall accuracy of the simulation.</div><div>To address this issue, we introduce a novel tool to improve the efficiency of a spectral reparametrization procedure able to ensure the optimal representation of such 3D objects and surface quantities, even when dealing with long time simulations. This new strategy makes the reparametrization technique fully adaptive, selecting only the frequencies that are needed to represent a given surface, improving the efficiency of the algorithm, preventing degradation in the quality of the simulation and enhancing the overall stability.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"142 ","pages":"Article 101308"},"PeriodicalIF":2.2,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528886","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 : 2025-11-11DOI: 10.1016/j.gmod.2025.101307
Michael Kofler, Michael Giritsch, Stefanie Elgeti
In this paper we present a lattice structure optimization approach by leveraging the capabilities of neural networks for implicit geometry representation. We employ the Deep Signed Distance Field (DeepSDF) method, where a continuous and low-dimensional latent space is introduced to encode the geometric information. In contrast to traditional topology optimization methods, this allows the restriction of the design space to specific geometries. In our case, the latent space is used to represent the geometry of different unit cells, that are stacked to form a lattice structure. Moreover, continuously varying the latent vector over the structure allows a functional grading and optimization. Unlike other lattice-structure optimization methods, we neither assume a large separation of scale nor periodicity. Instead, we perform a full-scale finite element analysis at each optimization step. The required mesh is obtained by a differentiable extension of the dual marching cubes algorithm, which enables gradient-based optimization.
在本文中，我们提出了一种利用神经网络的能力进行隐式几何表示的晶格结构优化方法。我们采用深度签名距离场（Deep Signed Distance Field, DeepSDF）方法，在该方法中引入一个连续的低维潜在空间来编码几何信息。与传统的拓扑优化方法相比，这种方法允许将设计空间限制为特定的几何形状。在我们的案例中，潜在空间用于表示不同单元格的几何形状，这些单元格堆叠形成晶格结构。此外，在结构上连续变化潜在向量允许功能分级和优化。与其他晶格结构优化方法不同，我们既没有假设大的尺度分离，也没有假设周期性。相反，我们在每个优化步骤中执行全面的有限元分析。通过对偶行进立方体算法的可微扩展获得所需的网格，从而实现基于梯度的优化。
{"title":"Structural optimization of lattice structures using deep neural networks as geometry representation","authors":"Michael Kofler, Michael Giritsch, Stefanie Elgeti","doi":"10.1016/j.gmod.2025.101307","DOIUrl":"10.1016/j.gmod.2025.101307","url":null,"abstract":"<div><div>In this paper we present a lattice structure optimization approach by leveraging the capabilities of neural networks for implicit geometry representation. We employ the Deep Signed Distance Field (DeepSDF) method, where a continuous and low-dimensional latent space is introduced to encode the geometric information. In contrast to traditional topology optimization methods, this allows the restriction of the design space to specific geometries. In our case, the latent space is used to represent the geometry of different unit cells, that are stacked to form a lattice structure. Moreover, continuously varying the latent vector over the structure allows a functional grading and optimization. Unlike other lattice-structure optimization methods, we neither assume a large separation of scale nor periodicity. Instead, we perform a full-scale finite element analysis at each optimization step. The required mesh is obtained by a differentiable extension of the dual marching cubes algorithm, which enables gradient-based optimization.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"142 ","pages":"Article 101307"},"PeriodicalIF":2.2,"publicationDate":"2025-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528885","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 : 2025-11-05DOI: 10.1016/j.gmod.2025.101303
Zhan Wang , Junhao Wang , Zongpu Li , Hao Su , Pei Lv , Mingliang Xu
AI-aided floorplan design is a longstanding task in computer graphics. However, most of the existing methods focus on generating floorplans by limited architecture-level elements (e.g., room sizes, positions, and adjacencies), which ignore environmental factors and do not support customized designs. In this paper, we propose FlexPlan, an interactive approach for high-flexibility floorplan design. In FlexPlan, we propose a novel graph structure, named ArchiGraph, which enables flexible editing more comprehensive layout elements (e.g., architectures, environments, human needs) in a floorplan. First, we match similar floorplans according to the input architecture and environment features. Then, leveraging ArchiGraph, we interactively produce rooms’ attributes and quickly output the vectorized floorplans. For ArchiGraph, we design a RelationNet to predict room adjacencies, and propose a BoxNet to generate high-quality room boxes. Subjective and objective experiments show that our method is compatible with generating diverse complex floorplans (e.g., floorplans with irregular layout boundaries and room shapes). Compared with the state-of-the-art methods, our method can produce higher quality floorplans, and increase the speed of layout generation by nearly 20 times at most.
{"title":"FlexPlan: High-flexibility interactive floorplan design based on ArchiGraph","authors":"Zhan Wang , Junhao Wang , Zongpu Li , Hao Su , Pei Lv , Mingliang Xu","doi":"10.1016/j.gmod.2025.101303","DOIUrl":"10.1016/j.gmod.2025.101303","url":null,"abstract":"<div><div>AI-aided floorplan design is a longstanding task in computer graphics. However, most of the existing methods focus on generating floorplans by limited architecture-level elements (e.g., room sizes, positions, and adjacencies), which ignore environmental factors and do not support customized designs. In this paper, we propose FlexPlan, an interactive approach for high-flexibility floorplan design. In FlexPlan, we propose a novel graph structure, named ArchiGraph, which enables flexible editing more comprehensive layout elements (e.g., architectures, environments, human needs) in a floorplan. First, we match similar floorplans according to the input architecture and environment features. Then, leveraging ArchiGraph, we interactively produce rooms’ attributes and quickly output the vectorized floorplans. For ArchiGraph, we design a RelationNet to predict room adjacencies, and propose a BoxNet to generate high-quality room boxes. Subjective and objective experiments show that our method is compatible with generating diverse complex floorplans (e.g., floorplans with irregular layout boundaries and room shapes). Compared with the state-of-the-art methods, our method can produce higher quality floorplans, and increase the speed of layout generation by nearly 20 times at most.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"142 ","pages":"Article 101303"},"PeriodicalIF":2.2,"publicationDate":"2025-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474232","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 : 2025-11-04DOI: 10.1016/j.gmod.2025.101306
Ruixi Ran, Wenlu Yang
Graph Convolutional Network (GCN) has achieved remarkable result in skeleton-based action recognition. In GCNs, multi-order information has shown notable improvement for recognition and the graph topology, which is the key to fusing and extracting representative features. However, the GCN-based methods still face the following problems: (1) Nodes will have over-smooth problems in deep and complex networks. (2) Lack of efficient methods to fuse data streams of different modalities. In this paper, we proposed a novel data-fusing method, Feedback Directed Graph Convolution (FD-GC), to dynamically construct diverse correlation matrices and effectively aggregate both joint and bone features in different hierarchical update state and utilize them as feedback loops to participate in aggregation respectively for both streams. Our methods significantly reduce the difficulty of modeling multi-streams features at a small parameter cost. Furthermore, the experimental results indicate FD-GC alleviates the over-smooth effect via the feedback mechanism, constructing stronger representation capabilities of fine-grained actions, and performs as well as most skeletal motion recognition algorithms on two large public datasets NTU RGB+D 60, NTU RGB+D 120 and Northwestern-UCLA.
{"title":"FD-GCN: Feedback Directed Graph Convolutional Network for skeleton-based action recognition","authors":"Ruixi Ran, Wenlu Yang","doi":"10.1016/j.gmod.2025.101306","DOIUrl":"10.1016/j.gmod.2025.101306","url":null,"abstract":"<div><div>Graph Convolutional Network (GCN) has achieved remarkable result in skeleton-based action recognition. In GCNs, multi-order information has shown notable improvement for recognition and the graph topology, which is the key to fusing and extracting representative features. However, the GCN-based methods still face the following problems: (1) Nodes will have over-smooth problems in deep and complex networks. (2) Lack of efficient methods to fuse data streams of different modalities. In this paper, we proposed a novel data-fusing method, Feedback Directed Graph Convolution (FD-GC), to dynamically construct diverse correlation matrices and effectively aggregate both joint and bone features in different hierarchical update state and utilize them as feedback loops to participate in aggregation respectively for both streams. Our methods significantly reduce the difficulty of modeling multi-streams features at a small parameter cost. Furthermore, the experimental results indicate FD-GC alleviates the over-smooth effect via the feedback mechanism, constructing stronger representation capabilities of fine-grained actions, and performs as well as most skeletal motion recognition algorithms on two large public datasets NTU RGB+D 60, NTU RGB+D 120 and Northwestern-UCLA.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"142 ","pages":"Article 101306"},"PeriodicalIF":2.2,"publicationDate":"2025-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474233","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}
The widespread application of metal additive manufacturing (AM) technologies has enabled exploration of complex design spaces to achieve optimally performing components. Current optimization techniques make use of several advanced methods, such as adjoint shape optimization, to provide designs that are superior to existing versions. However, they seldom discuss the manufacturability of the optimal designs. This research introduces novel restrictive design for AM (DfAM) constraints through computer-aided design (CAD) file modification which were used to guide the adjoint shape optimization process. The baseline design, using an application of a gas turbine fuel injector, was parameterized using non-uniform rational B-splines (NURBS) surface information stored in standard initial graphics exchange specification (IGES) file format. Gradient information computed using a commercial computational fluid dynamics (CFD) solver was used for NURBS shape modification in Python while focusing on imposing overhang angle and thin wall constraints for metal-AM. A method was developed to selectively replace information in the IGES file to accommodate modified design of surfaces of interest while preserving the overall geometry and maintain file integrity. The proposed framework accounts for varying levels of design complexity, accepting gradient information from commercial simulation software while imposing user-defined metal-AM constraints to obtain an optimal, additively manufacturable design. Findings from this study can be readily implemented in DfAM of any surface fluidic devices produced via metal laser AM, specifically Laser-Powder Bed Fusion.
{"title":"Developing novel restrictive design for additive manufacturing (DfAM) constraints for NURBS-based adjoint shape optimization for metal AM","authors":"Sagar Jalui, Jacqueline O’Connor, Yuan Xuan, Guha Manogharan","doi":"10.1016/j.gmod.2025.101304","DOIUrl":"10.1016/j.gmod.2025.101304","url":null,"abstract":"<div><div>The widespread application of metal additive manufacturing (AM) technologies has enabled exploration of complex design spaces to achieve optimally performing components. Current optimization techniques make use of several advanced methods, such as adjoint shape optimization, to provide designs that are superior to existing versions. However, they seldom discuss the manufacturability of the optimal designs. This research introduces novel restrictive design for AM (DfAM) constraints through computer-aided design (CAD) file modification which were used to guide the adjoint shape optimization process. The baseline design, using an application of a gas turbine fuel injector, was parameterized using non-uniform rational B-splines (NURBS) surface information stored in standard initial graphics exchange specification (IGES) file format. Gradient information computed using a commercial computational fluid dynamics (CFD) solver was used for NURBS shape modification in Python while focusing on imposing overhang angle and thin wall constraints for metal-AM. A method was developed to selectively replace information in the IGES file to accommodate modified design of surfaces of interest while preserving the overall geometry and maintain file integrity. The proposed framework accounts for varying levels of design complexity, accepting gradient information from commercial simulation software while imposing user-defined metal-AM constraints to obtain an optimal, additively manufacturable design. Findings from this study can be readily implemented in DfAM of any surface fluidic devices produced via metal laser AM, specifically Laser-Powder Bed Fusion.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"142 ","pages":"Article 101304"},"PeriodicalIF":2.2,"publicationDate":"2025-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333524","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 : 2025-10-13DOI: 10.1016/j.gmod.2025.101305
Xiang Gao, Qingyang Zhang, Chunye Gong, Chao Li, Xiaowei Guo, Jie Liu
Cartesian mesh-based fluid simulation methods are gaining popularity due to their fully automated mesh generation capabilities for geometries without repair. The performance and flexibility of Cartesian mesh generation significantly influence their application across various fields. This study introduces an efficient adaptive Cartesian mesh generation framework directly for arbitrary geometries. Initially, we propose a robust, high-quality build-in tessellation method and compute proximity. Subsequently, we design a hierarchical storage method combined with binary search for efficient intersection determination. To enhance flexibility, a fully unstructured data type and compressed data representation are established. Finally, we develop a four-step refinement mechanism to achieve geometric adaptation and smooth transitions effectively. The robustness and efficiency of the approach were validated through typical case studies, demonstrating that the mesh generation process for complex models can reach speeds of up to cells per second, which presents significant potential to address the challenges of real-time simulations.
{"title":"Efficient adaptive Cartesian mesh generation for complex boundary representation models","authors":"Xiang Gao, Qingyang Zhang, Chunye Gong, Chao Li, Xiaowei Guo, Jie Liu","doi":"10.1016/j.gmod.2025.101305","DOIUrl":"10.1016/j.gmod.2025.101305","url":null,"abstract":"<div><div>Cartesian mesh-based fluid simulation methods are gaining popularity due to their fully automated mesh generation capabilities for geometries without repair. The performance and flexibility of Cartesian mesh generation significantly influence their application across various fields. This study introduces an efficient adaptive Cartesian mesh generation framework directly for arbitrary geometries. Initially, we propose a robust, high-quality build-in tessellation method and compute proximity. Subsequently, we design a hierarchical storage method combined with binary search for efficient intersection determination. To enhance flexibility, a fully unstructured data type and compressed data representation are established. Finally, we develop a four-step refinement mechanism to achieve geometric adaptation and smooth transitions effectively. The robustness and efficiency of the approach were validated through typical case studies, demonstrating that the mesh generation process for complex models can reach speeds of up to <span><math><mrow><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mn>5</mn></mrow></msup></mrow></math></span> cells per second, which presents significant potential to address the challenges of real-time simulations.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"142 ","pages":"Article 101305"},"PeriodicalIF":2.2,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145333525","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}
In medical imaging detection of oral Cone Beam Computed Tomography (CBCT), there exist tiny lesions that are challenging to detect with low accuracy. The existing detection models are relatively complex. To address this, this paper presents a dual-stage YOLO detection method improved based on YOLOv8. Specifically, we first reconstruct the backbone network based on MobileNetV3 to enhance computational speed and efficiency. Second, we improve detection accuracy from three aspects: we design a composite feature fusion network to enhance the model’s feature extraction capability, addressing the issue of decreased detection accuracy for small lesions due to the loss of shallow information during the fusion process; we further combine spatial and channel information to design the C2f-SCSA module, which delves deeper into the lesion information. To tackle the problem of limited types and insufficient samples of lesions in existing CBCT images, our team collaborated with a professional dental hospital to establish a high-quality dataset, which includes 15 types of lesions and over 2000 accurately labeled oral CBCT images, providing solid data support for model training. Experimental results indicate that the improved method enhances the accuracy of the original algorithm by 3.5 percentage points, increases the recall rate by 4.7 percentage points, and raises the mean Average Precision (mAP) by 3.3 percentage points, a computational load of only 7.6 GFLOPs. This demonstrates a significant advantage in intelligent diagnosis of full-mouth lesions while improving accuracy and reducing computational load.
{"title":"An improved algorithm for full-mouth lesion detection based on YOLOv8","authors":"Xinchen Jiao , Shanshan Gao , Faqiang Huang , WenHan Dou , YuanFeng Zhou , Caiming Zhang","doi":"10.1016/j.gmod.2025.101302","DOIUrl":"10.1016/j.gmod.2025.101302","url":null,"abstract":"<div><div>In medical imaging detection of oral Cone Beam Computed Tomography (CBCT), there exist tiny lesions that are challenging to detect with low accuracy. The existing detection models are relatively complex. To address this, this paper presents a dual-stage YOLO detection method improved based on YOLOv8. Specifically, we first reconstruct the backbone network based on MobileNetV3 to enhance computational speed and efficiency. Second, we improve detection accuracy from three aspects: we design a composite feature fusion network to enhance the model’s feature extraction capability, addressing the issue of decreased detection accuracy for small lesions due to the loss of shallow information during the fusion process; we further combine spatial and channel information to design the C2f-SCSA module, which delves deeper into the lesion information. To tackle the problem of limited types and insufficient samples of lesions in existing CBCT images, our team collaborated with a professional dental hospital to establish a high-quality dataset, which includes 15 types of lesions and over 2000 accurately labeled oral CBCT images, providing solid data support for model training. Experimental results indicate that the improved method enhances the accuracy of the original algorithm by 3.5 percentage points, increases the recall rate by 4.7 percentage points, and raises the mean Average Precision (mAP) by 3.3 percentage points, a computational load of only 7.6 GFLOPs. This demonstrates a significant advantage in intelligent diagnosis of full-mouth lesions while improving accuracy and reducing computational load.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"142 ","pages":"Article 101302"},"PeriodicalIF":2.2,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269914","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}
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