Graphical Models最新文献_第3页

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 : 2025-09-01 Epub 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

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 : 2025-09-01 Epub 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":"2025-09-01","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

Carvable packing of revolved 3D objects for subtractive manufacturing 用于减法制造的旋转三维物体的可切割包装

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.2025.101282

Chengdong Wei, Shuai Feng, Hao Xu, Qidong Zhang, Songyang Zhang, Zongzhen Li, Changhe Tu, Haisen Zhao

引用次数: 0

GPU-accelerated rendering of vector strokes with piecewise quadratic approximation gpu加速绘制的矢量笔画与分段二次逼近

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.2025.101295

Xuhai Chen , Guangze Zhang , Wanyi Wang , Juan Cao , Zhonggui Chen

{"title":"GPU-accelerated rendering of vector strokes with piecewise quadratic approximation","authors":"Xuhai Chen , Guangze Zhang , Wanyi Wang , Juan Cao , Zhonggui Chen","doi":"10.1016/j.gmod.2025.101295","DOIUrl":"10.1016/j.gmod.2025.101295","url":null,"abstract":"<div><div>Vector graphics are widely used in areas such as logo design and digital painting, including both stroked and filled paths as primitives. GPU-based rendering for filled paths already has well-established solutions. Due to the complexity of stroked paths, existing methods often render them by approximating strokes with filled shapes. However, the performance of existing methods still leaves room for improvement. This paper designs a GPU-accelerated rendering algorithm along with a curvature-guided parallel adaptive subdivision method to accurately and efficiently render stroke areas. Additionally, we propose an efficient Newton iteration-based method for arc-length parameterization of quadratic curves, along with an error estimation technique. This enables a parallel rendering approach for dashed stroke styles and arc-length guided texture filling. Experimental results show that our method achieves average speedups of <span><math><mrow><mn>3</mn><mo>.</mo><mn>4</mn><mo>×</mo></mrow></math></span> for rendering quadratic stroked paths and <span><math><mrow><mn>2</mn><mo>.</mo><mn>5</mn><mo>×</mo></mrow></math></span> for rendering quadratic dashed strokes, compared to the best existing approaches.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101295"},"PeriodicalIF":2.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829843","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

Real-time neural soft shadow synthesis from hard shadows 实时神经软阴影从硬阴影合成

IF 2.2 4区计算机科学

Graphical Models Pub Date : 2025-09-01 Epub Date: 2025-08-14 DOI: 10.1016/j.gmod.2025.101294

Ran Chen , Xiang Xu , KaiYao Ge , Yanning Xu , Xiangxu Meng , Lu Wang

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 : 2025-09-01 Epub 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":"2025-09-01","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

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 : 2025-09-01 Epub 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":"2025-09-01","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

TerraCraft: City-scale generative procedural modeling with natural languages TerraCraft：使用自然语言的城市规模生成过程建模

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.2025.101285

Zichen Xi , Zhihao Yao , Jiahui Huang , Zi-Qi Lu , Hongyu Yan , Tai-Jiang Mu , Zhigang Wang , Qun-Ce Xu

{"title":"TerraCraft: City-scale generative procedural modeling with natural languages","authors":"Zichen Xi , Zhihao Yao , Jiahui Huang , Zi-Qi Lu , Hongyu Yan , Tai-Jiang Mu , Zhigang Wang , Qun-Ce Xu","doi":"10.1016/j.gmod.2025.101285","DOIUrl":"10.1016/j.gmod.2025.101285","url":null,"abstract":"<div><div>Automated generation of large-scale 3D scenes presents a significant challenge due to the resource-intensive training and datasets required. This is in sharp contrast to the 2D counterparts that have become readily available due to their superior speed and quality. However, prior work in 3D procedural modeling has demonstrated promise in generating high-quality assets using the combination of algorithms and user-defined rules. To leverage the best of both 2D generative models and procedural modeling tools, we present TerraCraft, a novel framework for generating geometrically high-quality 3D city-scale scenes. By utilizing Large Language Models (LLMs), TerraCraft can generate city-scale 3D scenes from natural text descriptions. With its intuitive operation and powerful capabilities, TerraCraft enables users to easily create geometrically high-quality scenes readily for various applications, such as virtual reality and game design. We validate TerraCraft’s effectiveness through extensive experiments and user studies, showing its superior performance compared to existing baselines.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101285"},"PeriodicalIF":2.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771658","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

DP-Adapter: Dual-pathway adapter for boosting fidelity and text consistency in customizable human image generation DP-Adapter：用于在可定制的人类图像生成中提高保真度和文本一致性的双路径适配器

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.2025.101292

Ye Wang , Ruiqi Liu , Xuping Xie , Lanjun Wang , Zili Yi , Rui Ma

{"title":"DP-Adapter: Dual-pathway adapter for boosting fidelity and text consistency in customizable human image generation","authors":"Ye Wang , Ruiqi Liu , Xuping Xie , Lanjun Wang , Zili Yi , Rui Ma","doi":"10.1016/j.gmod.2025.101292","DOIUrl":"10.1016/j.gmod.2025.101292","url":null,"abstract":"<div><div>With the growing popularity of personalized human content creation and sharing, there is a rising demand for advanced techniques in customized human image generation. However, current methods struggle to simultaneously maintain the fidelity of human identity and ensure the consistency of textual prompts, often resulting in suboptimal outcomes. This shortcoming is primarily due to the lack of effective constraints during the simultaneous integration of visual and textual prompts, leading to unhealthy mutual interference that compromises the full expression of both types of input. Building on prior research that suggests visual and textual conditions influence different regions of an image in distinct ways, we introduce a novel Dual-Pathway Adapter (DP-Adapter) to enhance both high-fidelity identity preservation and textual consistency in personalized human image generation. Our approach begins by decoupling the target human image into visually sensitive and text-sensitive regions. For visually sensitive regions, DP-Adapter employs an Identity-Enhancing Adapter (IEA) to preserve detailed identity features. For text-sensitive regions, we introduce a Textual-Consistency Adapter (TCA) to minimize visual interference and ensure the consistency of textual semantics. To seamlessly integrate these pathways, we develop a Fine-Grained Feature-Level Blending (FFB) module that efficiently combines hierarchical semantic features from both pathways, resulting in more natural and coherent synthesis outcomes. Additionally, DP-Adapter supports various innovative applications, including controllable headshot-to-full-body portrait generation, age editing, old-photo to reality, and expression editing. Extensive experiments demonstrate that DP-Adapter outperforms state-of-the-art methods in both visual fidelity and text consistency, highlighting its effectiveness and versatility in the field of human image generation.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"141 ","pages":"Article 101292"},"PeriodicalIF":2.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842158","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

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 : 2025-09-01 Epub 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":"2025-09-01","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