Graphical Models最新文献_第2页

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

Structural optimization of lattice structures using deep neural networks as geometry representation 使用深度神经网络作为几何表示的晶格结构优化

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-12-01 Epub Date: 2025-11-11 DOI: 10.1016/j.gmod.2025.101307

Michael Kofler, Michael Giritsch, Stefanie Elgeti

引用次数: 0

FlexPlan: High-flexibility interactive floorplan design based on ArchiGraph FlexPlan：基于ArchiGraph的高灵活性交互式平面图设计

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-12-01 Epub Date: 2025-11-05 DOI: 10.1016/j.gmod.2025.101303

Zhan Wang , Junhao Wang , Zongpu Li , Hao Su , Pei Lv , Mingliang Xu

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

引用次数: 0

Collision-free path planning method for digital orthodontic treatment 指指正畸治疗的无碰撞路径规划方法

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-09-01 Epub Date: 2025-08-15 DOI: 10.1016/j.gmod.2025.101297

Hao Yu , Longdu Liu , Shuangmin Chen , Lin Lu , Yuanfeng Zhou , Shiqing Xin , Changhe Tu

{"title":"Collision-free path planning method for digital orthodontic treatment","authors":"Hao Yu , Longdu Liu , Shuangmin Chen , Lin Lu , Yuanfeng Zhou , Shiqing Xin , Changhe Tu","doi":"10.1016/j.gmod.2025.101297","DOIUrl":"10.1016/j.gmod.2025.101297","url":null,"abstract":"<div><div>The rapid evolution of digital orthodontics has highlighted a critical need for automated treatment planning systems that balance computational efficiency with clinical reliability. However, existing methods still suffer from several limitations, including excessive clinician involvement (accounting for over 35% of treatment planning time), reliance on empirically defined key frames, and limited biomechanical plausibility, particularly in cases of severe dental crowding. This paper proposes a novel collision-free optimization framework to address these issues simultaneously. Our method defines a total movement energy function evaluated over each tooth’s pose at intermediate time frames. This energy is minimized iteratively using a steepest descent strategy. A rollback mechanism is employed: if inter-tooth penetration is detected during an update, the step size is halved repeatedly until collisions are eliminated. The framework allows flexible control over the number of intermediate frames to enforce a strict constraint on per-tooth displacement, limiting it to 0.2 mm translation or <span><math><mrow><mn>2</mn><mo>°</mo></mrow></math></span> rotation every 10 to 14 days. Clinical evaluations show that the proposed algorithm can generate desirable and clinically valid tooth movement plans, even in complex cases, while significantly reducing the need for manual intervention.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101297"},"PeriodicalIF":2.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842157","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

Feature line extraction based on winding number 基于圈数的特征线提取

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-09-01 Epub Date: 2025-08-13 DOI: 10.1016/j.gmod.2025.101296

Shuxian Cai , Juan Cao , Bailin Deng , Zhonggui Chen

引用次数: 0

Position-free multiple-scattering computations for micrograin BSDF model 微颗粒BSDF模型的无位置多次散射计算

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-09-01 Epub Date: 2025-08-05 DOI: 10.1016/j.gmod.2025.101288

Fangfang Zhou , Haiyu Shen , Mingzhen Li , Ying Zhao , Chongke Bi

{"title":"Position-free multiple-scattering computations for micrograin BSDF model","authors":"Fangfang Zhou , Haiyu Shen , Mingzhen Li , Ying Zhao , Chongke Bi","doi":"10.1016/j.gmod.2025.101288","DOIUrl":"10.1016/j.gmod.2025.101288","url":null,"abstract":"<div><div>Porous materials (e.g., weathered stone, industrial coatings) exhibit complex optical effects due to their micrograin and pore structures, posing challenges for photorealistic rendering. Explicit geometry models struggle to characterize their micrograin distributions at microscopic scales, while single-scattering microfacet model fails to accurately capture the multiple-scattering effects and causes energy non-conservation artifacts, manifesting as unrealistic luminance decay. We propose an enhanced micrograin BSDF model that accurately accounts for multiple scattering. First, we introduce a visible normal distribution function (VNDF) sampling method via rejection sampling. Building on VNDF sampling, we derive a position-free microsurface formulation incorporating both inter-micrograin and micrograin-to-base interactions. Furthermore, we propose a practical random walk method to simulate microsurface scattering, which accurately solves the derived formulation. Our micrograin BSDF model effectively eliminates the energy loss artifacts inherent in the previous model while significantly reducing noise, providing a physically accurate yet artistically controllable solution for rendering porous materials.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101288"},"PeriodicalIF":2.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771657","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

Adaptive mesh-aligned Gaussian Splatting for monocular human avatar reconstruction 自适应网格对齐高斯飞溅单目人体头像重建

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-09-01 Epub Date: 2025-08-25 DOI: 10.1016/j.gmod.2025.101300

Hai Yuan , Xia Yuan , Yanli Liu , Guanyu Xing , Jing Hu , Xi Wu , Zijun Zhou

{"title":"Adaptive mesh-aligned Gaussian Splatting for monocular human avatar reconstruction","authors":"Hai Yuan , Xia Yuan , Yanli Liu , Guanyu Xing , Jing Hu , Xi Wu , Zijun Zhou","doi":"10.1016/j.gmod.2025.101300","DOIUrl":"10.1016/j.gmod.2025.101300","url":null,"abstract":"<div><div>Virtual human avatars are essential for applications such as gaming, augmented reality, and virtual production. However, existing methods struggle to achieve high fidelity reconstruction from monocular input while keeping hardware costs low. Many approaches rely on the SMPL body prior and apply vertex offsets to represent clothed avatars. Unfortunately, excessive offsets often cause misalignment and blurred contours, particularly around clothing wrinkles, silhouette boundaries, and facial regions. To address these limitations, we propose a dual branch framework for human avatar reconstruction from monocular video. A lightweight Vertex Align Net (VAN) predicts per-vertex normal direction offsets on the SMPL mesh to achieve coarse geometric alignment and guide Gaussian-based human avatar modeling. In parallel, we construct a high resolution facial Gaussian branch based on FLAME estimated parameters, with facial regions localized via pretrained detectors. The facial and body renderings are fused using a semantic mask to enhance facial clarity and ensure globally consistent avatar appearance. Experiments demonstrate that our method surpasses state of the art approaches in modeling animatable human avatars with fine grained fidelity.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101300"},"PeriodicalIF":2.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893147","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

Disambiguating flat spots in discrete scalar fields 离散标量场中的平点消歧

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-09-01 Epub Date: 2025-08-27 DOI: 10.1016/j.gmod.2025.101299

L. Rocca , F. Iuricich , E. Puppo

引用次数: 0

Domain-Incremental Learning Paradigm for scene understanding via Pseudo-Replay Generation 通过伪回放生成的场景理解领域增量学习范式

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-09-01 Epub Date: 2025-08-11 DOI: 10.1016/j.gmod.2025.101290

Zhifeng Xie , Rui Qiu , Qile He , Mengtian Li , Xin Tan

{"title":"Domain-Incremental Learning Paradigm for scene understanding via Pseudo-Replay Generation","authors":"Zhifeng Xie , Rui Qiu , Qile He , Mengtian Li , Xin Tan","doi":"10.1016/j.gmod.2025.101290","DOIUrl":"10.1016/j.gmod.2025.101290","url":null,"abstract":"<div><div>Scene understanding is a computer vision task that involves grasping the pixel-level distribution of objects. Unlike most research focuses on single-scene models, we consider a more versatile proposal: domain-incremental learning for scene understanding. This allows us to adapt well-studied single-scene models into multi-scene models, reducing data requirements and ensuring model flexibility. However, domain-incremental learning that leverages correlations between scene domains has yet to be explored. To address this challenge, we propose a Domain-Incremental Learning Paradigm (D-ILP) for scene understanding, along with a new strategy of Pseudo-Replay Generation (PRG) that does not require manual labeling. Specifically, D-ILP leverages pre-trained single-scene models and incremental images for supervised training to acquire new knowledge from other scenes. As a pre-trained generation model, PRG can controllably generate pseudo-replays resembling source images from incremental images and text prompts. These pseudo-replays are utilized to minimize catastrophic forgetting in the original scene. We perform experiments with three publicly accessible models: Mask2Former, Segformer, and DeepLabv3+. With successfully transforming these single-scene models into multi-scene models, we achieve high-quality parsing results for original and new scenes simultaneously. Meanwhile, the validity and rationality of our method are proved by the analysis of D-ILP.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101290"},"PeriodicalIF":2.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810321","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