IEEE transactions on visualization and computer graphics最新文献

Error-Bounded Adaptive Generalized Winding Number Evaluation for B-Reps. b - rep的误差有界自适应广义圈数估计。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-19 DOI: 10.1109/TVCG.2026.3744785

Jiajie Guo, Ming Li

{"title":"Error-Bounded Adaptive Generalized Winding Number Evaluation for B-Reps.","authors":"Jiajie Guo, Ming Li","doi":"10.1109/TVCG.2026.3744785","DOIUrl":"https://doi.org/10.1109/TVCG.2026.3744785","url":null,"abstract":"The generalized winding number (GWN) offers a robust solution for point membership classification (PMC), tolerating geometric imperfections such as non-watertight boundaries. While GWN evaluation on discrete representations (e.g., meshes) has demonstrated its efficiency, its computation on B-Reps with trimmed surfaces remains computationally expensive, hindering practical use. We present a fast GWN evaluation framework for B-Reps with bounded error. The key idea is to adaptively discretize boundary curves with explicit analysis on deviation bounds. With the consistent discrete representations, queries inside and outside the surface bounding boxes are distinguished, and errors on the shared curves of adjacent surfaces are canceled out. Queries outside surface bounding boxes are reduced to efficient mesh-based evaluations with bounded error, which further enables efficient spatial acceleration. In addition, an optimized spherical projection that is both fast and accurate is designed for the remaining queries in boxes. Numerical experiments demonstrate the approach's superior efficiency while avoiding misclassification, compared with SOTA GWN approaches, and classic open-source CAD kernel Open CASCADE.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149225632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sketch2HOI: Sketch-Based Modeling of 3D Hand-Object Interaction with Hierarchical Attentive Fusion. Sketch2HOI：基于草图的三维手-对象交互建模与层次关注融合。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-14 DOI: 10.1109/TVCG.2026.3743494

Cong Zhu, Yuhua Ge, Nenglun Chen, Kao Zhang, Peibei Cao, Zhigeng Pan

{"title":"Sketch2HOI: Sketch-Based Modeling of 3D Hand-Object Interaction with Hierarchical Attentive Fusion.","authors":"Cong Zhu, Yuhua Ge, Nenglun Chen, Kao Zhang, Peibei Cao, Zhigeng Pan","doi":"10.1109/TVCG.2026.3743494","DOIUrl":"10.1109/TVCG.2026.3743494","url":null,"abstract":"Accurate modeling of 3D hand-object interactions plays a crucial role in modern 3D animation, serving as an essential component in animated movies and video game development. Traditional workflows require artists to manually adjust hand joints to create hand-object interactions, which is labor intensive and inefficient. We propose a novel framework for modeling 3D hand-object interactions from free-hand sketches. Our method generates physically plausible hand poses while ensuring precise alignment with user inputs. To achieve this, we introduce a pipeline that attentively integrates 2D sketch and 3D object information to model dexterous hand interactions. The core design principle is effective fusion of 2D and 3D features with explicit awareness of hand-object contact relationships. The overall framework mainly consists of three components, including a voxel-based feature fusion module for multi-modal representation, a hierarchical attentive prediction module for modeling long-range dependencies while capturing fine-grained details and a graph-based contact-aware adjustment module for refinement. Our method achieves strong performance and generalizes well to novel object categories. These components together yield a robust, user-friendly sketch-based 3D hand object interaction modeling system. Extensive experiments have verified the superiority of our proposed method compared with state-of-the-art works.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Computation of Restricted Voronoi Diagrams Using Point-in-Cell Tests. 利用单元中点测试高效计算受限Voronoi图。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-12 DOI: 10.1109/TVCG.2026.3742626

Yanyang Xiao, Juan Cao, Zhonggui Chen

{"title":"Efficient Computation of Restricted Voronoi Diagrams Using Point-in-Cell Tests.","authors":"Yanyang Xiao, Juan Cao, Zhonggui Chen","doi":"10.1109/TVCG.2026.3742626","DOIUrl":"https://doi.org/10.1109/TVCG.2026.3742626","url":null,"abstract":"We present a novel and efficient method for computing restricted Voronoi diagrams-that is, Voronoi diagrams constrained by a given domain. The intersection of the domain with a Voronoi cell (domain-cell intersection) is obtained by successively clipping the domain against the cell boundaries. Unlike existing approaches, we introduce an edge-based search scheme to identify the clipping planes (bisectors). A point-in-cell test is first employed to determine whether a spatial point lies inside a target Voronoi cell. Subsequently, for each edge of the intermediate domain-cell intersection, a clipping operation is performed only when its two endpoints lie inside and outside the corresponding Voronoi cell, respectively, and the required bisector can be found via a few point-in-cell tests. Consequently, our method performs exclusively the clippings that contribute to the final result, offering a substantial advantage over state-of-the-art approaches. Furthermore, since each domain-cell intersection can be computed independently, we extend our method to GPUs for parallel Voronoi diagram computation. Experimental results demonstrate that our method consistently outperforms state-of-the-art approaches, irrespective of the site distribution.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced Optical Axis Visualization in Polarization-Sensitive OCT Using Density-Encoded Line Integral Convolution. 利用密度编码线积分卷积增强偏振敏感OCT光轴可视化。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-09 DOI: 10.1109/TVCG.2026.3737078

Darven Murali Tharan, Marco Bonesi, Daniel Everett, Cushla McGoverin, Sue McGlashan, Ashvin Thambyah, Frederique Vanholsbeeck

引用次数: 0

The Now and Then: Integrating Current and Historical Data in Small Multiple Time Series Visualization. 现在和那时：在小的多时间序列可视化中集成当前和历史数据。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-09 DOI: 10.1109/TVCG.2026.3740740

Sydney K Purdue, Enrico Bertini, Melanie Tory

{"title":"The Now and Then: Integrating Current and Historical Data in Small Multiple Time Series Visualization.","authors":"Sydney K Purdue, Enrico Bertini, Melanie Tory","doi":"10.1109/TVCG.2026.3740740","DOIUrl":"10.1109/TVCG.2026.3740740","url":null,"abstract":"Small multiple time series visualizations are often used for real-time data monitoring tasks in high-impact domains such as healthcare and manufacturing. Effective design is critical because users rely on these visualizations to monitor data from many entities, such as patients or machines, often while distracted. Users may need to rapidly appraise current values for each entity, monitoring for those that go outside an acceptable range, while also watching temporal trends. However, no design guidelines currently exist for visually emphasizing current values in historical time series represented by small multiples. Via an iterative design process informed by a review of related literature and theory on visual channels and emphasis, we present a design space for glanceable time series small multiple displays. We evaluate this space through two online empirical studies, testing against non-threshold and threshold rapid appraisal tasks. Our results provide insights into merging current value and historical data visualizations for rapid appraisal tasks in time series monitoring. For non-threshold tasks, we found that size encodings on the current value, spatially integrated into the line chart, may provide a good compromise, with 28% response time improvement for tasks involving finding large current values and minimal interference with trend lookup tasks. More generally, integrated designs outperformed separated designs (in which the current value representation is spatially separated from the historical trend line). For threshold tasks, color threshold encodings significantly outperformed shaded band encodings. All supplemental materials are available at https://osf.io/wzf6a.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LightCrafter: A Structured Framework for Function-Aware Indoor Lighting Design. lightcraft：一个功能感知室内照明设计的结构化框架。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-08 DOI: 10.1109/TVCG.2026.3741423

Jingjing Liu, Chenchen Jing, Rui Tang, Youyi Zheng, Kun Zhou

{"title":"LightCrafter: A Structured Framework for Function-Aware Indoor Lighting Design.","authors":"Jingjing Liu, Chenchen Jing, Rui Tang, Youyi Zheng, Kun Zhou","doi":"10.1109/TVCG.2026.3741423","DOIUrl":"https://doi.org/10.1109/TVCG.2026.3741423","url":null,"abstract":"Indoor lighting design involves complex spatial, functional, and aesthetic considerations, making it challenging to achieve satisfactory results without expertise. Poor lighting distribution-such as excessive glare or uneven illumination-misalignment with scene-specific functional requirements like over- or under-illumination, and inefficient control systems significantly undermine both energy efficiency and lighting quality. In this work, we come up with a structured framework for function-aware indoor lighting design that formulates lighting design as a structured decision-making problem. Considering the scene as a hierarchical scene graph that captures the spatial and semantic relationships between scene elements, we use a pre-trained LLM to reconstruct the scene into a lighting structure graph under the guidance of a lighting knowledge base, containing nodes with lighting requirements and possible lighting nodes. Subsequently, in the lighting simulation engine, reinforcement learning is used to optimize the parameter values of the lighting nodes in the lighting structure graph to meet the lighting design requirements. Extensive quantitative and qualitative experiments show that our method outperforms existing methods for automatic indoor lighting design. It not only generates high-quality lighting designs, but also perceives the functions of the scene, allowing users to easily achieve functionally controllable indoor lighting designs through structured scene editing.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149140050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VisTR: Visualizations as Representations for Time-Series Table Reasoning. 可视化作为时间序列表推理的表示。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-07 DOI: 10.1109/TVCG.2026.3741131

Jianing Hao, Zhuowen Liang, Chunting Li, Yuyu Luo, Jie Li, Wei Zeng

{"title":"VisTR: Visualizations as Representations for Time-Series Table Reasoning.","authors":"Jianing Hao, Zhuowen Liang, Chunting Li, Yuyu Luo, Jie Li, Wei Zeng","doi":"10.1109/TVCG.2026.3741131","DOIUrl":"10.1109/TVCG.2026.3741131","url":null,"abstract":"Time-series table reasoning interprets temporal patterns and relationships in data to answer user queries. Despite recent advancements leveraging large language models (LLMs), existing methods often struggle with pattern recognition, context loss in long time-series data, and the lack of visual-based reasoning capabilities. To address these challenges, this paper proposes VisTR, a framework that places visualizations at the core of the reasoning. By transforming tables into fixed-size visualization references, VisTR captures key trends, anomalies, and temporal relationships, facilitating intuitive and interpretable reasoning. These visualizations are aligned with user input,i.e., charts, text, and sketches, through a fine-tuned multimodal LLM, ensuring robust cross-modal alignment. To handle large-scale data, VisTR integrates pruning and indexing mechanisms for scalable and efficient retrieval. Finally, an interactive visualization interface supports seamless multimodal exploration, enabling users to interact with data through textual and visual modalities. Quantitative comparisons with time-series matching methods and multimodal LLMs demonstrate the effectiveness of VisTR in aligning multimodal inputs, improving reasoning accuracy, and reducing latency. Furthermore, representative usage scenarios and user feedback validate VisTR's applicability to various time-series reasoning and exploration tasks.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149128674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Q-PETAL: Quality-aware Parameter-Efficient Text-to-Image Assessment Learning for Perceptual and Alignment Disentanglement. Q-PETAL：用于感知和对齐解纠缠的质量感知参数高效文本到图像评估学习。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-07 DOI: 10.1109/TVCG.2026.3740565

Rehan Saeed, Syed Muhammad Israr, Semere Gerezgiher Tesfay, Rana Muhammad Usman, Feng Zhao

{"title":"Q-PETAL: Quality-aware Parameter-Efficient Text-to-Image Assessment Learning for Perceptual and Alignment Disentanglement.","authors":"Rehan Saeed, Syed Muhammad Israr, Semere Gerezgiher Tesfay, Rana Muhammad Usman, Feng Zhao","doi":"10.1109/TVCG.2026.3740565","DOIUrl":"https://doi.org/10.1109/TVCG.2026.3740565","url":null,"abstract":"The rapid proliferation of Text-to-Image (T2I) models across domains such as advertising, education, art, and entertainment has created an urgent need for robust, scalable evaluation metrics. However, existing quality assessment methods rely on specialized models for each quality dimension, lacking robustness and scalability. In this study, we propose Q-PETAL, a parameter-efficient and holistic framework for T2I quality assessment. Q-PETAL includes: (1) quality-aware adaptation to attention projections via low-rank matrix decomposition; (2) patch-level embeddings to evaluate fine-grained perceptual features; (3) semantic-aware text-image alignment using a structured prompt ensemble and cross-modal cosine scoring. A learnable scalar balances the trade-off between text-image alignment and perceptual quality to produce an overall score. Despite having significantly fewer trainable parameters, Q-PETAL outperforms state-of-the-art methods, generalizes well across a wide range of quality variations, and achieves inference latency comparable to that of zero-shot CLIP. Hence, Q-PETAL is an effective and scalable solution for the evolving challenges in T2I generation.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149139994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Active Reconstruction of Radiance Fields with Expanded Silhouette Refinement. 主动重建辐射场与扩展轮廓细化。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-07 DOI: 10.1109/TVCG.2026.3741385

Ruiqi Li, Yiu-Ming Cheung, Mengke Li, Zhikai Hu

{"title":"Active Reconstruction of Radiance Fields with Expanded Silhouette Refinement.","authors":"Ruiqi Li, Yiu-Ming Cheung, Mengke Li, Zhikai Hu","doi":"10.1109/TVCG.2026.3741385","DOIUrl":"10.1109/TVCG.2026.3741385","url":null,"abstract":"Radiance field representations have achieved notable success in novel view synthesis and 3D geometry reconstruction. Nevertheless, the reconstruction quality is highly sensitive to both the number of input images and the extent of scene coverage, and it often requires manual effort to capture well-conditioned image collections. To reduce acquisition cost while preserving reconstruction fidelity, active view selection methods aim to determine the next-best-view (NBV) that maximizes information gain at each iteration. However, existing approaches typically restrict the camera pose search space during NBV optimization, which can lead to sub-optimal view selection and limited reconstruction quality. In this paper, we expand the NBV candidates sampling space to identify informative image collections that jointly balance local surface detail and global geometric completeness. We propose a two-stage Silhouette Refinement Candidate Generation (SRCG) framework that leverages prior knowledge of well-composed viewpoints to generate candidate views with improved object coverage. We optimize the coarse camera candidates in a differentiable manner through volume rendering, guided by a proposed Expanded Silhouette Loss that encourages effective silhouette capture. Extensive evaluations on synthetic and real-world benchmarks using Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) backbones demonstrate that the proposed approach produces candidate views with superior surface coverage and pixel density and consistently improves novel view synthesis across diverse object categories.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149129779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LatRef-Diff: Latent and Reference-Guided Diffusion for Facial Attribute Editing and Style Manipulation. 面部属性编辑和风格处理的潜在和参考引导扩散。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-10-06 DOI: 10.1109/TVCG.2026.3740083

Wenmin Huang, Weiqi Luo, Xiaochun Cao, Jiwu Huang

{"title":"LatRef-Diff: Latent and Reference-Guided Diffusion for Facial Attribute Editing and Style Manipulation.","authors":"Wenmin Huang, Weiqi Luo, Xiaochun Cao, Jiwu Huang","doi":"10.1109/TVCG.2026.3740083","DOIUrl":"https://doi.org/10.1109/TVCG.2026.3740083","url":null,"abstract":"Facial attribute editing and style manipulation are crucial for applications like virtual avatars and photo editing. However, achieving precise control over facial attributes without altering unrelated features is challenging due to the complexity of facial structures and the strong correlations between attributes. While conditional GANs have shown progress, they are limited by accuracy issues and training instability. Diffusion models, though promising, face challenges in style manipulation due to the limited expressiveness of semantic directions. In this paper, we propose LatRef-Diff, a novel diffusion-based framework that addresses these limitations. We replace the traditional semantic directions in diffusion models with style codes and propose two methods for generating them: latent and reference guidance. Based on these style codes, we design a style modulation module that integrates them into the target image, enabling both random and customized style manipulation. This module incorporates learnable vectors, cross-attention mechanisms, and a hierarchical design to improve accuracy and image quality. Additionally, to enhance training stability while eliminating the need for paired images (e.g., before and after editing), we propose a forward-backward consistency training strategy. This strategy first removes the target attribute approximately using image-specific semantic directions and then restores it via style modulation, guided by perceptual and classification losses. Extensive experiments on CelebA-HQ demonstrate that LatRef-Diff achieves state-of-the-art performance in both qualitative and quantitative evaluations. Ablation studies validate the effectiveness of our model's design choices.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149120581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0