Graphical Models最新文献

Simplification of locally refined gradient meshes 局部细化梯度网格的简化

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2026-03-01 Epub Date: 2026-03-13 DOI: 10.1016/j.gmod.2026.101322

E. Kato , T. Ophelders , A. Telea , J. Kosinka

引用次数: 0

Monte Carlo optimization for gradient meshes 梯度网格的蒙特卡罗优化

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2026-01-01 Epub Date: 2026-01-09 DOI: 10.1016/j.gmod.2026.101320

K. He, J.B.T.M. Roerdink, J. Kosinka

引用次数: 0

Visual simulation of fruit chilling injury 水果冷害的视觉模拟

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2026-01-01 Epub Date: 2025-11-22 DOI: 10.1016/j.gmod.2025.101309

Yixin Xu , Shiguang Liu

{"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":"2026-01-01","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}

引用次数: 0

Corrigendum to “LDM: Large tensorial SDF model for textured mesh generation” [Graphical Models, Volume 140, August 2025, 101271] “LDM：用于纹理网格生成的大张量SDF模型”的勘误表[图形模型，卷140，August 2025, 101271]

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2026-01-01 Epub Date: 2025-12-23 DOI: 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

引用次数: 0

EasyAnim: 3D facial animation from in-the-wild videos for avatars with customized riggings EasyAnim: 3D面部动画从野外视频与定制的装备的化身

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-12-01 Epub Date: 2025-09-23 DOI: 10.1016/j.gmod.2025.101298

Hao-Xuan Song , Yue Qian , Xiaohang Zhan , Tai-Jiang Mu

引用次数: 0

FD-GCN: Feedback Directed Graph Convolutional Network for skeleton-based action recognition 基于骨架动作识别的反馈有向图卷积网络

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-12-01 Epub Date: 2025-11-04 DOI: 10.1016/j.gmod.2025.101306

Ruixi Ran, Wenlu Yang

{"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":"2025-12-01","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}

引用次数: 0

Optimal representation of time-dependent spherical geometries 时变球面几何的最佳表示

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-12-01 Epub Date: 2025-11-14 DOI: 10.1016/j.gmod.2025.101308

Chiara Sorgentone

引用次数: 0

Efficient adaptive Cartesian mesh generation for complex boundary representation models 复杂边界表示模型的高效自适应笛卡尔网格生成

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-12-01 Epub Date: 2025-10-13 DOI: 10.1016/j.gmod.2025.101305

Xiang Gao, Qingyang Zhang, Chunye Gong, Chao Li, Xiaowei Guo, Jie Liu

{"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":"2025-12-01","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}

引用次数: 0

Developing novel restrictive design for additive manufacturing (DfAM) constraints for NURBS-based adjoint shape optimization for metal AM 基于nurbs的金属增材制造伴随形状优化约束设计研究

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-12-01 Epub Date: 2025-10-15 DOI: 10.1016/j.gmod.2025.101304

Sagar Jalui, Jacqueline O’Connor, Yuan Xuan, Guha Manogharan

{"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":"2025-12-01","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}

引用次数: 0

An improved algorithm for full-mouth lesion detection based on YOLOv8 基于YOLOv8的全口病变检测改进算法

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-12-01 Epub Date: 2025-10-06 DOI: 10.1016/j.gmod.2025.101302

Xinchen Jiao , Shanshan Gao , Faqiang Huang , WenHan Dou , YuanFeng Zhou , Caiming Zhang

{"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":"2025-12-01","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}

引用次数: 0