Graphical Models最新文献

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 : 2025-10-06 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":"2025-10-06","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}

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 : 2025-09-23 DOI: 10.1016/j.gmod.2025.101298

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

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 : 2025-08-27 DOI: 10.1016/j.gmod.2025.101299

L. Rocca , F. Iuricich , E. Puppo

引用次数: 0

Edge-aware denoising framework for real-time mobile ray tracing 实时移动光线追踪的边缘感知去噪框架

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-27 DOI: 10.1016/j.gmod.2025.101301

Haosen Fu, Mingcong Ma, Junqiu Zhu, Lu Wang, Yanning Xu

{"title":"Edge-aware denoising framework for real-time mobile ray tracing","authors":"Haosen Fu, Mingcong Ma, Junqiu Zhu, Lu Wang, Yanning Xu","doi":"10.1016/j.gmod.2025.101301","DOIUrl":"10.1016/j.gmod.2025.101301","url":null,"abstract":"<div><div>With the proliferation of mobile hardware-accelerated ray tracing, visual quality at low sampling rates (1spp) significantly deteriorates due to high-frequency noise and temporal artifacts introduced by Monte Carlo path tracing. Traditional spatiotemporal denoising methods, such as Spatiotemporal Variance-Guided Filtering (SVGF), effectively suppress noise by fusing multi-frame information and using geometry buffer (G-buffer) guided filters. However, their reliance on per-frame variance computation and global filtering imposes prohibitive overhead for mobile devices. This paper proposes an edge-aware, data-driven real-time denoising architecture within the SVGF framework, tailored explicitly for mobile computational constraints. Our method introduces two key innovations that eliminate variance estimation overhead: (1) an adaptive filtering kernel sizing mechanism, which dynamically adjusts filtering scope based on local complexity analysis of the G-buffer; and (2) a data-driven weight table construction strategy, converting traditional computational processes into efficient real-time lookup operations. These innovations significantly enhance processing efficiency while preserving edge accuracy. Experimental results on the Qualcomm Snapdragon 768G platform demonstrate that our method achieves 55 FPS with 1spp input. This <strong>frame rate is 67.42% higher</strong> than mobile-optimized SVGF, provides <strong>better visual quality</strong>, and <strong>reduces power consumption by 16.80%</strong>. Our solution offers a practical and efficient denoising framework suitable for real-time ray tracing in mobile gaming and AR/VR applications.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101301"},"PeriodicalIF":2.2,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904092","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 : 2025-08-25 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":"2025-08-25","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

Efficient RANSAC in 4D Plane Space for Point Cloud Registration 高效的四维平面空间RANSAC点云配准

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-22 DOI: 10.1016/j.gmod.2025.101289

Chang Liu , Chao Liu , Yuming Zhang , Zhongqi Wu , Jianwei Guo

{"title":"Efficient RANSAC in 4D Plane Space for Point Cloud Registration","authors":"Chang Liu , Chao Liu , Yuming Zhang , Zhongqi Wu , Jianwei Guo","doi":"10.1016/j.gmod.2025.101289","DOIUrl":"10.1016/j.gmod.2025.101289","url":null,"abstract":"<div><div>3D registration methods based on point-level information struggle in situations with noise, density variation, large-scale points, and small overlaps, while existing primitive-based methods are usually sensitive to tiny errors in the primitive extraction process. In this paper, we present a reliable and efficient global registration algorithm exploiting the RANdom SAmple Consensus (RANSAC) in the plane space instead of the point space. To improve the inlier ratio in the putative correspondences, we design an inner plane-based descriptor, termed <em>Convex Hull Descriptor</em> (CHD), and an inter plane-based descriptor, termed <em>PLane Feature Histograms</em> (PLFH), which take full advantage of plane contour shape and plane-wise relationship, respectively. Based on those new descriptors, we randomly select corresponding plane pairs to compute candidate transformations, followed by a hypotheses verification step to identify the optimal registration. Extensive tests on large-scale point sets demonstrate the effectiveness of our method, and that it notably improves registration performance compared to state-of-the-art methods in terms of efficiency and accuracy.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101289"},"PeriodicalIF":2.2,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144886852","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

DISCO: Efficient Diffusion Solver for large-scale Combinatorial Optimization problems 大规模组合优化问题的高效扩散求解器

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-21 DOI: 10.1016/j.gmod.2025.101284

Hang Zhao , Kexiong Yu , Yuhang Huang , Renjiao Yi , Chenyang Zhu , Kai Xu

{"title":"DISCO: Efficient Diffusion Solver for large-scale Combinatorial Optimization problems","authors":"Hang Zhao , Kexiong Yu , Yuhang Huang , Renjiao Yi , Chenyang Zhu , Kai Xu","doi":"10.1016/j.gmod.2025.101284","DOIUrl":"10.1016/j.gmod.2025.101284","url":null,"abstract":"<div><div>Combinatorial Optimization (CO) problems are fundamentally important in numerous real-world applications across diverse industries, notably computer graphics, characterized by entailing enormous solution space and demanding time-sensitive response. Despite recent advancements in neural solvers, their limited expressiveness struggles to capture the multi-modal nature of CO landscapes. While some research has adopted diffusion models, these methods sample solutions indiscriminately from the entire NP-complete solution space with time-consuming denoising processes, limiting scalability for large-scale problems. We propose <strong>DISCO</strong>, an efficient <strong>DI</strong>ffusion <strong>S</strong>olver for large-scale <strong>C</strong>ombinatorial <strong>O</strong>ptimization problems that excels in both solution quality and inference speed. DISCO’s efficacy is twofold: First, it enhances solution quality by constraining the sampling space to a more meaningful domain guided by solution residues, while preserving the multi-modal properties of the output distributions. Second, it accelerates the denoising process through an analytically solvable approach, enabling solution sampling with very few reverse-time steps and significantly reducing inference time. This inference-speed advantage is further amplified by Jittor, a high-performance learning framework based on just-in-time compiling and meta-operators. DISCO delivers strong performance on large-scale Traveling Salesman Problems and challenging Maximal Independent Set benchmarks, with inference duration up to 5.38 times faster than existing diffusion solver alternatives. We apply DISCO to design 2D/3D TSP Art, enabling the generation of fluid stroke sequences at reduced path costs. By incorporating DISCO’s multi-modal property into a divide-and-conquer strategy, it can further generalize to solve unseen-scale instances out of the box.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101284"},"PeriodicalIF":2.2,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878547","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

DIFF: A dataset for indoor flexible furniture DIFF：室内柔性家具的数据集

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-19 DOI: 10.1016/j.gmod.2025.101293

Jia-Hong Liu , Shao-Kui Zhang , Shuran Sun , Zihao Wang , Song-Hai Zhang

引用次数: 0

Nav2Scene: Navigation-driven fine-tuning for robot-friendly scene generation Nav2Scene：用于机器人友好场景生成的导航驱动微调

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-08-17 DOI: 10.1016/j.gmod.2025.101287

Bowei Jiang , Tongyuan Bai , Peng Zheng , Tieru Wu , Rui Ma

{"title":"Nav2Scene: Navigation-driven fine-tuning for robot-friendly scene generation","authors":"Bowei Jiang , Tongyuan Bai , Peng Zheng , Tieru Wu , Rui Ma","doi":"10.1016/j.gmod.2025.101287","DOIUrl":"10.1016/j.gmod.2025.101287","url":null,"abstract":"<div><div>The integration of embodied intelligence in indoor scene synthesis holds significant potential for future interior design applications. Nevertheless, prevailing methodologies for indoor scene synthesis predominantly adhere to data-driven learning paradigms. Despite achieving photorealistic 3D renderings through such approaches, current frameworks systematically neglect to incorporate agent-centric functional metrics essential for optimizing navigational topology and task-oriented interactivity in embodied AI systems like service robotics platforms or autonomous domestic assistants. For example, poorly arranged furniture may prevent robots from effectively interacting with the environment, and this issue cannot be fully resolved by merely introducing prior constraints. To fill this gap, we propose Nav2Scene, a novel plug-and-play fine-tuning mechanism that can be deployed on existing scene generators to enhance the suitability of generated scenes for efficient robot navigation. Specifically, we first introduce path planning score (PPS), which is defined based on the results of the path planning algorithm and can be used to evaluate the robot navigation suitability of a given scene. Then, we pre-compute the PPS of 3D scenes from existing datasets and train a ScoreNet to efficiently predict the PPS of the generated scenes. Finally, the predicted PPS is used to guide the fine-tuning of existing scene generators and produce indoor scenes with higher PPS, indicating improved suitability for robot navigation. We conduct experiments on the 3D-FRONT dataset for different tasks including scene generation, completion and re-arrangement. The results demonstrate that by incorporating our Nav2Scene mechanism, the fine-tuned scene generators can produce scenes with improved navigation compatibility for home robots, while maintaining superior or comparable performance in terms of scene quality and diversity.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101287"},"PeriodicalIF":2.2,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858475","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 : 2025-08-15 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":"2025-08-15","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