IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICSIEEE可视化与计算机图形学汇刊
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS(英文缩写 IEEE T VIS COMPUT GR),ISSN 1077-2626,eISSN 1941-0506,中文译名:IEEE可视化与计算机图形学汇刊 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 5.226 | Q1 |
| 2022 | 5.200 | Q1 |
| 2023 | 4.700 | Q1 |
| 2024 | 6.500 | Q1 |
| 2025 | 6.800 | Q1 |
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 最新收录文献
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1. Diffusing Heat: Generative Scan Path Optimization for Laser Powder Bed Fusion.
PMID:日期:2026-10-01Island scanning is widely adopted in Laser Powder Bed Fusion (LPBF) to mitigate residual stress and distortion. The global visiting order of islands still governs the layer-wise thermal history. Existing geometric heuristics are efficient but largely ignore history-dependent heat accumulation on irregular topologies, while physics-driven global optimization requires repeated thermal evaluation and is typically computationally expensive for online planning. We present Diffusing Heat, a diffusion-model-based planner that generates heat-aware island sequences for complex layer geometries. We first couple a high throughput Gaussian-kernel thermal surrogate with simulated annealing to build an expert dataset under a long-horizon thermal-uniformity objective that penalizes both sustained non uniformity and peak overheating events. Conditioned on the set of active island centroids, a conditional diffusion model denoises a permutation-like successor representation in parallel and produces score matrices that are close to valid permutations. At inference time, sampled scores are decoded into feasible open scan paths and further improved by a lightweight objective consistent refinement stage under the same surrogate metric. Extensive simulated evaluations demonstrate that our planner consistently improves thermal uniformity over representative heuristic baselines, while approaching the solution quality of offline combinatorial optimization and accelerating the planning process by over 20 times. Furthermore, physical LPBF experiments show our planner reduces maximum distortion by up to 53% compared to standard geometric patterns.
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2. A Room to Roam: Real-Time Reset Prediction for Redirected Walking Based on Environment Layouts.
2. 可漫游空间：基于环境布局的实时重置预测重定向行走PMID:日期:2026-09-29Redirected Walking (RDW) allows users to explore large virtual environments by using overt resets to reorient users and prevent collisions. However, frequent resets increase the risk of cybersickness and reduce presence. Real physical environments are usually cluttered, and the complexity of their layouts affects the reset frequency. Physical spaces usually contain various objects, some fixed (e.g., built-ins) and some movable (e.g., furniture), creating opportunities to reduce the need for resets by rearranging movable objects. Systematically evaluating alternative layouts is challenging because repeated user testing is costly, and simulation-based approaches are often too slow for interactive design. We introduce a deep-learning predictor that estimates the expected number of resets directly from top view layout maps of physical and virtual environments. Trained on large-scale simulation data, the model generalizes to unseen layouts and provides interactive predictions at a rate of 105 milliseconds per layout. We embedded the predictor in a layout-editing interface and compared three interfaces (intuition-driven, simulation-based, and prediction-based) in a user study. Participants reached the pre-specified reset target with all three interfaces. With the prediction-based interface, they reached it in less time, with lower workload and higher usability. Additionally, consultations with four RDW experts emphasized effort, sequencing constraints, and object semantics, and recommended localized readouts showing where resets occur. These results suggest that continuous reset estimates reduce the time and workload needed to prepare a cluttered space for RDW.
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3. Disentangling Visual and Factual Correctness in LVLMs' Visualization Literacy.
3. 解耦LVLM可视化素养中的视觉与事实正确性PMID:日期:2026-09-29Large Vision-Language Models (LVLMs) have shown remarkable capabilities in visualization interpretation, yet it remains unclear whether their responses reflect genuine reasoning over visual evidence or the influence of factual priors learned during training. Current evaluation methods mix these two sources, obscuring when correct visual interpretation is overridden by memorized factual knowledge. We present a disentanglement framework that systematically isolates visual correctness from factual correctness, revealing fundamental validity limitations in existing visualization literacy assessments. Through three complementary experiments with 15 state-of-theart LVLMs, we demonstrate that: (1) Although several models achieve human-level performance on standard tests (VLAT), such performance may reflect factual recall rather than visual understanding, whereas randomized-data tests (reVLAT) underestimate visualization literacy when visual interpretation is correct but superseded by conflicting factual priors. (2) Using our Counterfactual Visualization Literacy Assessment Test (CVLAT) alongside capability-normalized arbitration metrics, we classify models by the sign of their visual-factual reliance index (VFRI). This classification reveals a visualization-oriented majority and a factual knowledge-oriented minority, although several near-zero cases warrant cautious interpretation. The factual knowledgeoriented minority tends to override the chart with prior knowledge. A human baseline (N = 30) on the same counterfactual items confirms that people overwhelmingly follow the chart under conflict, providing a human reference point for visual-factual arbitration. (3) Prompt-based intervention can shift this prioritization, but its effectiveness is highly model-dependent and often direction-asymmetric, with some models responding strongly to only one prompt direction. Furthermore, high chart-reading capability does not predict prompt-controllability, indicating that visual-factual arbitration is not uniformly steerable. Overall, our findings demonstrate that LVLMs' high visualization accuracy is not sufficient evidence of faithful visual reasoning. We argue that reliable LVLM integration into visual analytics requires evaluating not only visualization literacy, but also how models arbitrate between visual evidence and factual priors, particularly when the two sources diverge. The CVLAT benchmark and code are available at https://github.com/JaeyoungKim-HCIL/CVLAT.
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4. Novel Encoding and Decoding Methods for Enhancing Short-Time High-Frequency VR-SSVEP Performance.
4. 用于增强短时高频VR-SSVEP性能的新型编码与解码方法PMID:日期:2026-09-29Virtual reality (VR) brain-computer interfaces (BCIs) based on steady-state visually evoked potentials (SSVEPs) face a critical challenge: achieving high information transfer rates (ITR) requires short time windows, while user comfort necessitates high-frequency stimulation. Decoding this high-frequency and short-time combination is highly challenging due to low signal-to-noise ratios. To address this, we applied the independent visual field stimulation (IVFS) paradigm to encode six high frequency targets (31-36 Hz) using three common encoding designs. Furthermore, we propose a novel Inter-class and Spatial Domain Collaborative Decoding (ICSD-CD) framework. By incorporating the Target-Band enhanced Spatial Decomposition Pattern (TB-eDSP) for lateralized feature extraction, ICSD CD synergistically integrates conventional inter-class difference (ICD) features with IVFS-specific spatial domain difference (SDD) features via a dynamic, confidence-based fusion strategy. Experimental results demonstrate that this framework effectively resolves the decoding bottleneck, achieving average accuracies of 89.11% (119.6 bits/min) with a 0.3 s data length and 93.93% (114 bits/min) with 0.5 s. This significantly outperforms single-domain methods, providing a viable pathway for high-performance VR BCIs.
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5. FrameTwin: Curve-Anchored Gaussian Alignment from Sparse Views for Adaptive Wireframe 3D Printing.
5. FrameTwin：基于稀疏视角的曲线锚定高斯对齐用于自适应线框3D打印PMID:日期:2026-09-23We present FrameTwin, a curve-anchored Gaussian alignment framework that uses sparse-view images to close the control loop for adaptive wireframe 3D printing. Our key idea is to capture the deformation of thin wireframe structures from sparse-view images using Gaussian kernels anchored to parametric curves, yielding a compact and geometry-aware encoding that explicitly captures strut topology. Driven by a differentiable rendering pipeline, FrameTwin estimates a neural deformation field that aligns the partially printed target model with the deformed structure observed during fabrication, where the optimized curve-Gaussian representation serves as a digital twin of the evolving wireframe. Unlike general Gaussian-splatting approaches, our formulation constrains kernel placement along parametric curves, substantially reducing the ambiguity inherent in sparse-view observations of thin structures. The resultant deformation-field alignment enforces global consistency across all struts. By using the estimated deformation field to blend the distorted printed geometry with the remaining unprinted geometry, FrameTwin enables adaptive updates to future printing trajectories. We demonstrate that FrameTwin can robustly capture and compensate for deformation in wireframe models fabricated using a robotized 3D printing system.
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6. Label-aware Gaussian Splatting for Medical Volume Visualization in Mixed Reality.
6. 面向混合现实医学体可视化的标签感知高斯泼溅PMID:日期:2026-09-23Volume visualization is an important tool in exploring and understanding 3D medical data. Though the recent 3D Gaussian Splatting (3DGS) offers great potential for realtime high-quality medical volume visualization, directly applying 3DGS to visualize medical volume data often overlooks important anatomical structures, especially for vascular structures that are vital in disease detection and surgical planning. This paper presents a new framework called Label-aware Gaussian Splatting (LAGS) to leverage the inherent structural information of medical volumes and optimize the Gaussian representations, especially for fine-grained details of medical volume data. First, we introduce a new label attribute for Gaussian points and propose fine label initialization to enhance subsequent Gaussian training. Then, we devise a Label-aware Rasterizer (LAR) consisting of dual branches of differentiable rasterizers to jointly optimize photorealistic and structural representations. Next, we develop Label-aware Densification (LAD) to adaptively supplement Gaussians in regions with inferior structural representation. The above modules work synergistically during Gaussian training to encourage accurate reconstruction of delicate anatomical structures. We generate a dataset called MedVol-GS including medical scenes from various volumes and evaluate our method on it. Experiments show that LAGS outperforms existing NeRF and GS-based methods in rendering quality, with over 3 dB improvement in PSNR on average. Qualitative results reveal that our method has advantages in reconstructing intricate geometric structures even with occlusion and preventing floaters in nonlabeled areas. In addition, we enable real-time interaction at 72 FPS with labeled structures of reconstructed Gaussians in mixed reality (MR) for intuitive observation. A user study demonstrates that LAGS significantly improves medical volume visualization quality and experience for both clinicians and engineers in MR environments.
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7. Neural Cone Radiosity for Interactive Global Illumination with Glossy Materials.
PMID:日期:2026-09-22Modeling of high-frequency outgoing radiance distributions has long been a key challenge in rendering, particularly for glossy materials. Such distributions concentrate radiative energy within a narrow lobe and are highly sensitive to changes in view direction. However, existing neural radiosity methods, which primarily rely on positional feature encoding, exhibit notable limitations in capturing these high-frequency, strongly view-dependent radiance distributions. To address this, we propose a highly-efficient approach by reflectance-aware ray cone encoding based on the neural radiosity framework, named neural cone radiosity. The core idea is to employ a pre-filtered multi-resolution hash grid to accurately approximate the glossy BSDF lobe, embedding view-dependent reflectance characteristics directly into the encoding process through continuous spatial aggregation. Our design not only significantly improves the network's ability to model high-frequency reflection distributions but also effectively handles surfaces with a wide range of glossiness levels, from highly glossy to low-gloss finishes. Meanwhile, our method reduces the network's burden in fitting complex radiance distributions, allowing the overall architecture to remain compact and efficient. Comprehensive experimental results demonstrate that our method consistently produces high-quality, visually clean renderings in real time under various glossiness conditions, and delivers superior fidelity and realism compared to baseline approaches.
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8. VIP-GS: Enhancing Sparse Novel View Synthesis via In-Place Virtual Viewpoint Inpainting.
8. VIP-GS：通过原位虚拟视角修复增强稀疏新视角合成PMID:日期:2026-09-22While radiance field representations have achieved remarkable success in photo-realistic novel view synthesis with densely captured images, their performance sharply degrades under sparse input conditions, resulting in floater artifacts and missing regions caused by its inherent shape-radiance ambiguities and lack of informative observations, respectively. To address these challenges, we propose a few-shot 3DGS framework that leverages scene-adapted generative priors gated by multi-view geometric consistency to enhance the reconstruction quality for both interpolated and extrapolated views given limited observing viewpoints. Built upon coarse 3D Gaussians initiated via multi-view stereo, the core of our approach lies in lightweight personalized adaptation of a pretrained inpainting model from only the sparse input images, requiring no task-specific paired training data. During scene reconstruction, the current 3DGS reconstruction itself localizes unreliable virtual-view regions through multi-view warping consistency, where our personalized inpainting model is selectively invoked, and the repaired views are iteratively integrated to enrich the training corpus of Gaussian Splatting, progressively expanding the observing coverage and intensifying multi-view constraints. Extensive experimental results on three popular benchmark datasets including Tanks and Temples, MVImgNet and MipNeRF-360 demonstrate that our method, without using any specialized external training assets, dramatically improves the rendering quality of sparse-view novel view synthesis with a single consumer-grade GPU.
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9. Initialize to Generalize: A Stronger Initialization Pipeline for Sparse-View 3DGS.
9. 初始化以泛化：一种更强的稀疏视图3DGS初始化流程PMID:日期:2026-09-22Sparse-view 3D Gaussian Splatting (3DGS) often overfits to the training views, leading to artifacts like blurring in novel view rendering. Prior work addresses it either by enhancing the initialization (i.e., the point cloud from Structure-from-Motion (SfM)) or by adding training-time constraints (regularization) to the 3DGS optimization. Yet our controlled ablations reveal that initialization is the decisive factor: it determines the attainable performance band in sparse-view 3DGS, while training-time constraints act as complementary refinements whose effectiveness depends on the initial geometric prior. Given initialization's primacy, we focus our design there. Although SfM performs poorly under sparse views due to its reliance on feature matching, it still provides reliable seed points. Thus, building on SfM, our effort aims to supplement the regions it fails to cover as comprehensively as possible. Specifically, we design: (i) frequency-aware SfM that improves low-texture coverage via low-frequency view augmentation and relaxed multi-view correspondences; (ii) 3DGS self-initialization that lifts photometric supervision into additional points, compensating SfM-sparse regions with learned Gaussian centers; and (iii) point-cloud regularization that enforces multi-view consistency and uniform spatial coverage through simple geometric/visibility priors, yielding a clean and reliable point cloud. Our experiments on LLFF and Mip-NeRF360 demonstrate consistent gains in sparse-view settings, establishing our approach as a stronger initialization strategy.
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10. Proprioceptive and Gaze Adaptations to Virtual Leg Length Asymmetry.
10. 本体感觉与凝视对虚拟腿长不对称的适应PMID:日期:2026-09-21Proprioception is the sense of perceiving and predicting body position and movement in space. Together with vision and touch, it plays a central role in shaping the body image. Virtual reality allows users to embody self-avatars with altered morphologies, thereby modifying the body representation. This study investigated how embodying an avatar with a leg twice its original length affects proprioception, embodiment, and visual scanning measured through state and transition gaze entropy. Congruent and incongruent visuotactile stimuli were used to induce high and low levels of embodiment. Across 26 participants, embodiment was preserved despite the deformation, regardless of whether the dominant or non-dominant leg was altered. A proprioceptive drift corresponding to 78% of the virtual leg length was observed, independent of visuotactile congruence. While leg deformation alone did not affect gaze entropy, incongruent visuotactile stimuli reduced embodiment and increased both state and transition gaze entropy, reflecting increased top-down modulation of gaze control. These findings show that deformations of the virtual lower limbs can be integrated into the body image without perturbing sensory integration, providing insights for developing rehabilitation strategies targeting proprioceptive deficits.