Computers & Graphics-Uk最新文献

英文中文

Energy-based haptic rendering for real-time surgical simulation 基于能量的实时外科模拟触觉渲染

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-23 DOI: 10.1016/j.cag.2025.104524

Lei He , Mingbo Hu , Wenli Xiu , Hongyu Wu , Siming Zheng , Shuai Li , Qian Dong , Aimin Hao

Haptic-based surgical simulation is widely utilized for training surgical skills. However, simulating the interaction between rigid surgical instruments and soft tissues presents significant technical challenges. In this paper, we propose an energy-based haptic rendering method to achieve both large deformations and rigid–soft haptic interaction. Different from existing methods, both the rigid tools and soft tissues are modeled by an energy-based virtual coupling system. The constraints of soft deformation, tool-object interaction and haptic rendering are defined by potential energy. Benefit from energy-based constraints, we can realize complex surgical operations, such as inserting tools into soft tissue. The virtual coupling of soft tissue enables the separation of haptic interaction into two components: soft deformation with high computational complexity, and high-frequency haptic rendering. The soft deformation with shape constraints is accelerated GPU at a relatively low frequency(60Hz

\sim

100Hz), while the haptic rendering runs in another thread at a high frequency (

\geq

1000Hz). We have implemented haptic simulation for two commonly used surgical operations, pressing and pulling. The experimental results show that our method can achieve stable feedback force and non-penetration between the tool and soft tissue under the condition of large soft deformation.

基于触觉的手术模拟被广泛应用于外科技能的训练。然而，模拟刚性手术器械和软组织之间的相互作用提出了重大的技术挑战。在本文中，我们提出了一种基于能量的触觉渲染方法，以实现大变形和刚软触觉交互。与现有方法不同，该方法采用基于能量的虚拟耦合系统对刚性工具和软组织进行建模。软变形约束、工具-物体交互约束和触觉渲染约束由势能定义。得益于基于能量的约束，我们可以实现复杂的外科手术，例如将工具插入软组织。软组织的虚拟耦合使触觉交互分离为两个组成部分：具有高计算复杂度的软变形和高频触觉渲染。具有形状约束的软变形以相对较低的频率（60Hz ~ 100Hz）在GPU上加速，而触觉渲染以较高的频率（≥1000Hz）在另一个线程中运行。我们已经实现了两种常用的外科手术触觉模拟，按压和拉动。实验结果表明，在软变形较大的情况下，该方法可以实现刀具与软组织之间稳定的反馈力和不侵彻。

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引用次数: 0

MRescue: A mixed reality system for real-time navigation and rescue in complex multi-floor buildings MRescue：用于复杂多层建筑实时导航和救援的混合现实系统

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-22 DOI: 10.1016/j.cag.2025.104527

Gilda Manfredi, Nicola Capece, Ugo Erra

In high-rise buildings, complex layouts, and frequent structural modifications can make emergency rescues challenging. Conventional 2D rescue plans offer no real-time guidance and can be difficult for rescuers to interpret under stress. To overcome these limitations, we introduce an advanced Mixed Reality (MR) application designed for real-time rescue assistance in multi-floor buildings. Built for the Meta Quest 3, a cost-efficient standalone MR headset, our system enables users to scan, update, and navigate a dynamic 3D model of their surroundings. We implement external data storage and utilize spatial anchors to ensure accurate realignment to bypass the Meta Quest 3’s constraint of storing only 15 rooms. Additionally, the application utilizes the A* algorithm to dynamically calculate optimal routes based on the user’s real-time location, taking into account the room layout and any obstacles inside. Users can navigate using either a floating 3D minimap or a 2D minimap anchored to their left hand, with staircases seamlessly incorporated into navigation routes, including virtual door warnings and automatic removal of navigation cues near stairs for safety. To improve user experience, we have implemented hand tracking for interactions. We conducted a study with a large sample of participants to evaluate usability and effectiveness. This study included the NASA Task Load Index (NASA-TLX) to assess the cognitive load, along with the System Usability Scale (SUS), the Self-Assessment Manikin (SAM), the Single Ease Question (SEQ), task completion times, and a post-evaluation feedback questionnaire. The results demonstrate that the system achieves high usability, low cognitive workload, and positive user experience while supporting situational awareness, user confidence, and efficient navigation. The results indicate that a system based on an MR headset has the potential to improve situational awareness and decision-making in dynamic indoor environments.

在高层建筑中，复杂的布局和频繁的结构修改会给紧急救援带来挑战。传统的二维救援计划不能提供实时指导，救援人员在压力下很难理解。为了克服这些限制，我们引入了一种先进的混合现实（MR）应用程序，专为多层建筑的实时救援援助而设计。我们的系统是为Meta Quest 3打造的，这是一款经济高效的独立MR耳机，用户可以扫描、更新和导航周围环境的动态3D模型。我们实现了外部数据存储，并利用空间锚点来确保精确的重新排列，以绕过Meta Quest 3仅存储15个房间的限制。此外，该应用程序利用A*算法根据用户的实时位置动态计算最佳路线，考虑到房间布局和内部的任何障碍物。用户可以使用浮动的3D小地图或固定在左手的2D小地图进行导航，楼梯无缝地融入导航路线，包括虚拟门警告和自动移除楼梯附近的导航提示，以确保安全。为了改善用户体验，我们实现了对交互的手部跟踪。我们对大量参与者进行了一项研究，以评估可用性和有效性。本研究采用NASA任务负荷指数（NASA- tlx）评估认知负荷，同时采用系统可用性量表（SUS）、自我评估模型（SAM）、单一简易问题（SEQ）、任务完成时间和评估后反馈问卷。结果表明，该系统在支持态势感知、用户信心和高效导航的同时，实现了高可用性、低认知工作量和积极的用户体验。结果表明，基于MR头显的系统具有改善动态室内环境中态势感知和决策的潜力。

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引用次数: 0

Foreword to the special section on the 29th international ACM conference on 3D web technology 第29届国际ACM 3D web技术会议专题部分的前言

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-17 DOI: 10.1016/j.cag.2025.104523

A. Augusto de Sousa , Miguel Angel Guevara López , Traian Lavric

引用次数: 0

Foreword to special section on SIBGRAPI 2025 SIBGRAPI 2025特别部分的前言

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-17 DOI: 10.1016/j.cag.2025.104526

Leonardo Sacht, Marcos Lage, Ricardo Marroquim

引用次数: 0

Sparse-to-dense light field reconstruction based on Spatial–Angular Multi-Dimensional Interaction and Guided Residual Networks 基于空间-角度多维相互作用和制导残差网络的稀疏-稠密光场重建

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-16 DOI: 10.1016/j.cag.2025.104525

Haijiao Gu, Yan Piao

Dense light fields contain rich spatial and angular information, making them highly valuable for applications such as depth estimation, 3D reconstruction, and multi-view elemental image synthesis. Light-field cameras capture both spatial and angular scene information in a single shot. However, due to high hardware requirements and substantial storage costs, practical acquisitions often yield only sparse light-field maps. To address this problem, this paper proposes an efficient end-to-end sparse-to-dense light-field reconstruction method based on Spatial–Angular Multi-Dimensional Interaction and Guided Residual Networks. The Spatial–Angular Multi-Dimensional Interaction Module (SAMDIM) fully exploits the four-dimensional structural information of light-field image data in both spatial and angular domains. It performs dual-modal interaction across spatial and angular dimensions to generate dense subviews. The channel attention mechanism within the interaction module significantly improves the image quality of these dense subviews. Finally, the Guided Residual Refinement Module (GRRM) further enhances the texture details of the generated dense subviews, enhancing the reconstruction quality of the dense light field. Experimental results demonstrate that our proposed network model achieves clear advantages over state-of-the-art methods in both visual quality and quantitative metrics on real-world datasets.

密集光场包含丰富的空间和角度信息，对深度估计、三维重建和多视点元素图像合成等应用具有重要价值。光场相机在一次拍摄中捕捉空间和角度的场景信息。然而，由于高硬件要求和大量存储成本，实际获取往往只产生稀疏的光场地图。为了解决这一问题，本文提出了一种基于空间-角度多维交互和制导残差网络的端到端稀疏到密集光场重构方法。空间-角度多维交互模块（SAMDIM）充分利用了光场图像数据在空间和角度两个领域的四维结构信息。它在空间和角度维度上执行双模态交互，以生成密集的子视图。交互模块中的通道注意机制显著提高了这些密集子视图的图像质量。最后，利用制导残差细化模块（GRRM）对生成的密集子视图的纹理细节进行进一步增强，提高了密集光场的重建质量。实验结果表明，我们提出的网络模型在现实世界数据集的视觉质量和定量指标方面都比最先进的方法有明显的优势。

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引用次数: 0

Negotiating without turning: Exploring rear-space interaction for negotiated teleportation in VR 在不转弯的情况下谈判：在VR中探索谈判传送的后太空互动

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-16 DOI: 10.1016/j.cag.2025.104522

Hao-Zhong Yang , Wen-Tong Shu , Yi-Jun Li , Miao Wang

Social virtual reality enables multi-user co-presence and collaboration but introduces privacy challenges such as personal space intrusion and unwanted interruptions. Teleportation negotiation techniques help address these issues by allowing users to define teleportation-permitted zone, maintaining spatial boundaries and comfort. However, existing methods primarily focus on forward view and often require physical rotation to monitor and respond to requests originating from behind. This can disrupt immersion and reduce social presence.

To better understand these challenges, we first conducted a preliminary study to identify users’ needs for rear-space awareness during teleportation negotiation. Based on the findings, we designed two rear-awareness negotiation techniques, Window negotiation and MiniMap negotiation. These techniques display rear-space information within the forward view and allow direct interaction without excessive head movement. In a within-subjects study with 16 participants in a virtual museum, we compared these methods against a baseline front-facing approach. Results showed that MiniMap was the preferred technique, significantly improving spatial awareness, usability, and user comfort. Our findings emphasize the importance of integrating rear-space awareness in social VR negotiation systems to enhance interaction efficiency, comfort, and immersion.

社交虚拟现实支持多用户共同存在和协作，但引入了隐私挑战，如个人空间入侵和不必要的中断。隐形传态协商技术通过允许用户定义允许隐形传态的区域、保持空间边界和舒适度来帮助解决这些问题。然而，现有的方法主要关注前向视图，并且经常需要物理旋转来监视和响应来自后面的请求。这会破坏沉浸感并减少社交存在感。为了更好地理解这些挑战，我们首先进行了一项初步研究，以确定用户在隐形传态谈判期间对后太空感知的需求。在此基础上，我们设计了两种后感知谈判技术：窗口谈判和小地图谈判。这些技术在前方视野中显示后方空间信息，并允许直接交互而无需过度的头部运动。在一项有16名虚拟博物馆参与者的主题内研究中，我们将这些方法与基线的正面方法进行了比较。结果表明，MiniMap是首选技术，显著提高了空间意识、可用性和用户舒适度。我们的研究结果强调了在社交VR谈判系统中整合后空间感知对于提高交互效率、舒适度和沉浸感的重要性。

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引用次数: 0

Locomotion in CAVE: Enhancing immersion through full-body motion CAVE中的运动：通过全身运动增强沉浸感

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-06 DOI: 10.1016/j.cag.2025.104510

Xiaohui Li , Xiaolong Liu , Zhongchen Shi , Wei Chen , Liang Xie , Meng Gai , Jun Cao , Suxia Zhang , Erwei Yin

Cave Automatic Virtual Environment (CAVE) is one of the virtual reality (VR) immersive devices currently used to present virtual environments. However, the locomotion methods in the CAVE are limited by unnatural interaction methods, severely hindering the user experience and immersion in the CAVE. We proposed a locomotion framework for CAVE environments aimed at enhancing the immersive locomotion experience through optimized human motion recognition technology. Firstly, we construct a four-sided display CAVE system, then through the dynamic method based on Perspective-n-Point to calibrate the camera, using the obtained camera intrinsics and extrinsic parameters, and an action recognition architecture to get the action category. At last, transform the action category to a graphical workstation that renders display effects on the screen. We designed a user study to validate the effectiveness of our method. Compared to the traditional methods, our method has significant improvements in realness and self-presence in the virtual environment, effectively reducing motion sickness.

洞穴自动虚拟环境（Cave Automatic Virtual Environment，简称Cave）是目前用于呈现虚拟环境的虚拟现实（VR）沉浸式设备之一。然而，CAVE中的运动方式受到非自然交互方式的限制，严重阻碍了用户在CAVE中的体验和沉浸感。我们提出了一个洞穴环境的运动框架，旨在通过优化人体运动识别技术来增强沉浸式运动体验。首先，我们构建了一个四面显示的CAVE系统，然后通过基于Perspective-n-Point的动态方法对摄像机进行标定，利用得到的摄像机的内在参数和外在参数，以及动作识别体系结构得到动作类别。最后，将动作类别转换为在屏幕上呈现显示效果的图形工作站。我们设计了一个用户研究来验证我们方法的有效性。与传统方法相比，我们的方法在虚拟环境中的真实感和自我存在性方面有显著提高，有效地减少了晕动病。

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引用次数: 0

HARDER: 3D human avatar reconstruction with distillation and explicit representation 难度：具有蒸馏和显式表示的3D人类化身重建

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-05 DOI: 10.1016/j.cag.2025.104512

Chun-Hau Yu, Yu-Hsiang Chen, Cheng-Yen Yu, Li-Chen Fu

3D human avatar reconstruction has become a popular research field in recent years. Although many studies have shown remarkable results, most existing methods either impose overly strict data requirements, such as depth information or multi-view images, or suffer from significant performance drops in specific areas. To address these challenges, we propose HARDER. We combine the Score Distillation Sampling (SDS) technique with the designed modules, Feature-Specific Image Captioning (FSIC) and RADR (Region-Aware Differentiable Rendering), allowing the Latent Diffusion Model (LDM) to guide the reconstruction process, especially in unseen regions. Furthermore, we have developed various training strategies, including personalized LDM, delayed SDS, focused SDS, and multi-pose SDS, to make the training process more efficient.

Our avatars use an explicit representation that is compatible with modern computer graphics pipelines. Also, the entire reconstruction and real-time animation process can be completed on a single consumer-grade GPU, making this application more accessible.

近年来，三维人体虚拟形象重建已成为一个热门的研究领域。尽管许多研究已经取得了显著的成果，但现有的方法要么对数据要求过于严格，如深度信息或多视图图像，要么在特定领域性能下降明显。为了应对这些挑战，我们建议更加努力。我们将分数蒸馏采样（SDS）技术与设计的模块，特定特征图像标注（FSIC）和区域感知可微分渲染（RADR）相结合，允许潜在扩散模型（LDM）指导重建过程，特别是在看不见的区域。此外，我们还开发了多种训练策略，包括个性化LDM、延迟SDS、集中SDS和多姿态SDS，以提高训练过程的效率。我们的虚拟形象使用与现代计算机图形管道兼容的显式表示。此外，整个重建和实时动画过程可以在单个消费级GPU上完成，使此应用程序更易于访问。

引用次数: 0

NeRVis: Neural Radiance Field Model-Uncertainty Visualization 神经辐射场模型-不确定性可视化

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-04 DOI: 10.1016/j.cag.2025.104511

Kirsten W.H. Maas , Thiam-Wai Chua , Danny Ruijters , Nicola Pezzotti , Anna Vilanova

Neural Radiance Field (NeRF) is a promising deep learning approach for three-dimensional (3D) scene reconstruction and view synthesis, with various applications in fields like robotics and medical imaging. However, similar to other deep learning models, understanding NeRF model inaccuracies and their causes is challenging. The 3D nature of NeRFs further adds challenges such as identifying complex geometrical features and analyzing 2D views that suffer from object occlusions. Existing methods for uncertainty quantification (UQ) in NeRFs address the lack of NeRF model understanding by expressing uncertainty in model predictions, exposing limitations in model design or training data. However, these UQ techniques typically rely on quantitative evaluation that does not facilitate human interpretation. We introduce NeRVis, a visual analytics system that supports model users to explore and analyze uncertainty in NeRF scenes. NeRVis combines spatial uncertainty analysis with per-view uncertainty summaries, fostering analysis of the uncertainty in Lambertian NeRF scenes. As a proof-of-concept, we illustrate our approach using two UQ methods. We demonstrate the effectiveness of NeRVis with two different use scenarios, tackling key challenges in the NeRF UQ literature.

神经辐射场（NeRF）是一种很有前途的用于三维（3D）场景重建和视图合成的深度学习方法，在机器人和医学成像等领域有各种应用。然而，与其他深度学习模型类似，理解NeRF模型的不准确性及其原因是具有挑战性的。nerf的3D特性进一步增加了识别复杂几何特征和分析受物体遮挡影响的2D视图等挑战。现有的NeRF不确定性量化（UQ）方法通过表达模型预测中的不确定性来解决NeRF模型理解不足的问题，暴露了模型设计或训练数据的局限性。然而，这些UQ技术通常依赖于不便于人类解释的定量评估。我们介绍NeRVis，这是一个可视化分析系统，支持模型用户探索和分析NeRF场景中的不确定性。NeRVis将空间不确定性分析与每个视图的不确定性总结相结合，促进了Lambertian NeRF场景的不确定性分析。作为概念验证，我们使用两个UQ方法来说明我们的方法。我们通过两种不同的使用场景展示了NeRVis的有效性，解决了NeRF UQ文献中的关键挑战。

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引用次数: 0

Foreword to the special section on recent advances in graphics and interaction (RAGI 2025) 关于图形和交互的最新进展的特别部分的前言（RAGI 2025）

IF 2.8 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Computers & Graphics-Uk

Pub Date : 2025-12-04 DOI: 10.1016/j.cag.2025.104509

Tomás Alves, José Creissac Campos, Alan Chalmers

引用次数: 0

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