Eurographics Digital Library
Not a member yet
17121 research outputs found
Sort by
Fast and Invertible Simplicial Approximation of Magnetic-Following Interpolation for Visualizing Fusion Plasma Simulation Data
No full textWe introduce a fast and invertible approximation for fusion plasma simulation data represented as 2D planar meshes with connectivities approximating magnetic field lines along the toroidal dimension in deformed 3D toroidal spaces. Scientific variables (e.g., density and temperature) in these fusion data are interpolated following a complex magnetic-field-line-following scheme in the toroidal space represented by a cylindrical coordinate system. This deformation in the 3D space poses challenges for root-finding and interpolation. To this end, we propose a novel paradigm for visualizing and analyzing such data based on a newly developed algorithm for constructing a 3D simplicial mesh within the deformed 3D space. Our algorithm generates a tetrahedral mesh that connects the 2D meshes using tetrahedra while adhering to the constraints on node connectivities imposed by the magnetic field-line scheme. Specifically, we first divide the space into smaller partitions to reduce complexity based on the input geometries and constraints on connectivities. Then, we independently search for a feasible tetrahedralization of each partition, considering nonconvexity. We demonstrate our method with two X-Point Gyrokinetic Code (XGC) simulation datasets on the International Thermonuclear Experimental Reactor (ITER) and Wendelstein 7-X (W7-X), and use an ocean simulation dataset to substantiate broader applicability of our method. An open source implementation of our algorithm is available at https://github.com/rcrcarissa/DeformedSpaceTet.Computer Graphics ForumVolume and Colo
Towards a Software Framework for Evaluating the Visualization Literacy of Large Language Models
No full textLarge Language Models (LLMs) are increasingly integrated into Natural Language Interfaces (NLIs) for visualizations, enabling users to inquire about visualizations through natural language. This work introduces a software framework for evaluating LLMs' visualization literacy, i.e., their ability to interpret and answer questions about visualizations. Our framework generates a set of data points across different LLMs, prompts, and question types, allowing for in-depth analysis. We demonstrate its utility by two experiments, examining the impact of the temperature parameter and predefined answer choices.EuroVis 2025 - PostersPoster
Modeling and Measuring the Chart Communication Recall Process
No full textUnderstanding memory in the context of data visualizations is paramount for effective design. While immediate clarity in a visualization is crucial, retention of its information determines its long-term impact. While extensive research has underscored the elements enhancing visualization memorability, a limited body of work has delved into modeling the recall process. This study investigates the temporal dynamics of visualization recall, focusing on factors influencing recollection, shifts in recall veracity, and the role of participant demographics. Using data from an empirical study (n = 104), we propose a novel approach combining temporal clustering and handcrafted features to model recall over time. A long short-term memory (LSTM) model with attention mechanisms predicts recall patterns, revealing alignment with informativeness scores and participant characteristics. Our findings show that perceived informativeness dictates recall focus, with more informative visualizations eliciting narrative-driven insights and less informative ones prompting aesthetic-driven responses. Recall accuracy diminishes over time, particularly for unfamiliar visualizations, with age and education significantly shaping recall emphases. These insights advance our understanding of visualization recall, offering practical guidance for designing visualizations that enhance retention and comprehension. All data and materials are available at: https://osf.io/ghe2j/.Computer Graphics ForumEvaluation and Guidanc
Uncertainty Visualization in Medical Education: Utilizing Novel Teaching Technologies to Enhance Clinical Decision-Making
No full textUncertainty is an inherent aspect of medical decision-making, influencing diagnostics, treatment planning, and prognosis. While uncertainty visualization can aid clinicians in interpreting probabilistic data and supporting shared decision-making with patients, traditional medical education often overlooks data interpretation and visualization training. This gap can hinder clinicians' ability to navigate and communicate complex medical data, potentially affecting patient care. To address this challenge, we developed a structured course that integrates new technologies, hybrid learning models, and practical visualization tools based on generative AI to teach uncertainty visualization effectively. By equipping clinicians with these skills, our approach aims to enhance evidence-based decision-making, improve communication of uncertain data, and ultimately foster better clinical outcomes.EuroVis 2025 - Education PapersEducation Papers Session
Visual Agentic System for Spatial Metric Query Answering in Remote Sensing Images
No full textAccurately measuring real-world object dimensions from Remote Sensing (RS) images is crucial for applications in geospatial analysis and urban planning. Traditional Vision-Language Models (VLMs) struggle with spatial reasoning, while end-to-end remote sensing VLMs are often limited to predefined tasks such as image captioning. In this paper, we propose a visual agentic system for spatial metric query answering, dynamically integrating code-generation agents with a grounded remote sensing VLM and a Vision Specialist. Our system autonomously identifies reference objects, infers scale factors, and performs spatial measurements through structured subroutines. Experiments demonstrate that our approach achieves higher accuracy in footprint area estimation compared to state-of-the-art large language models with vision capabilities.Eurographics 2025 - PostersPoster
When meshes Lie: Tracing Flaws and Extracting Knowledge from Expert Intervention in CH Mesh Processing
No full textIn the digital acquisition of Cultural Heritage artefacts, surface corrections are routinely performed by expert operators to eliminate defects in reconstructed 3D models. Yet these interventions, though essential, are rarely documented or formalised. This work proposes a method to capture and structure them: corrections are semantically tagged during the mesh cleaning phase and retroprojected onto the pre-cleaned model, transforming both meshes into a dual-layer system of interpretive paradata. By treating correction as a moment of knowledge production rather than mere refinement, the framework enables the construction of a taxonomy of flaws grounded in morphological traits and geometric indicators. The result is a reproducible and extensible system for flaw recognition that supports both expert practice and future analytical generalisation.Digital HeritagePoster
Remaking Lost Communities in Virtual Cultural Landscapes
No full textCharacters in immersive, Virtual Reality environments have the potential to enrich the user experience, improving engagement with heritage, and in doing so, benefiting heritage organisations and their communities. By creating authentic digital scenes based upon archaeological and historical data, we enable these communities and their visitors to better understand the past. Often, historical reconstructions can appear empty, focused on the landscape and architecture, yet omitting animals, people and associated intangible heritage. We demonstrate the potential of enriching these reconstructions with the details of lives past.Digital HeritageReconstructing the Pas
Preserving the Sacred In Situ: A Scalable Model for Hybrid Religious Heritage Documentation
No full textAn interdisciplinary approach underpins the PaReS project (Painted Relic Shrines in Situ), which explores medieval painted reliquary shrines preserved in situ in Belgian churches. Combining high-resolution 3D photogrammetry, non-invasive scientific imaging (IRR, X-ray, MA-XRF), physically based rendering (PBR), art historical and archival reasearch, the project documents and analyses these fragile, hybrid heritage objects situated at the intersection of materiality, devotion, and historical memory. A methodological contribution lies in the integration of three complementary domains-historical documentation, digital acquisition, and scientific analysis-into a reproducible, site-specific protocol. This framework has already revealed material and stylistic transformations over time. For instance, modelling of the Shrine of St Eucherius indicated phased construction, while IRR findings on the Shrine of St Livinus challenge its conventional dating. By making data accessible via open-access platform and engaging students and local communities, PaReS combines academic rigour with public outreach. Its workflow offers a transferable model for the documentation and conservation of sacred heritage.Digital HeritageAnalysing and Documenting Digitized Asset
Content-Aware Texturing for Gaussian Splatting
No full textGaussian Splatting has become the method of choice for 3D reconstruction and real-time rendering of captured real scenes. However, fine appearance details need to be represented as a large number of small Gaussian primitives, which can be wasteful when geometry and appearance exhibit different frequency characteristics. Inspired by the long tradition of texture mapping, we propose to use texture to represent detailed appearance where possible. Our main focus is to incorporate per-primitive texture maps that adapt to the scene in a principled manner during Gaussian Splatting optimization. We do this by proposing a new appearance representation for 2D Gaussian primitives with textures where the size of a texel is bounded by the image sampling frequency and adapted to the content of the input images. We achieve this by adaptively upscaling or downscaling the texture resolution during optimization. In addition, our approach enables control of the number of primitives during optimization based on texture resolution. We show that our approach performs favorably in image quality and total number of parameters used compared to alternative solutions for textured Gaussian primitives.Eurographics Symposium on RenderingGaussian
Tactile Embroidery Reproduction Exploiting Machine Vision for Visually Impaired Engagement
No full textBest conservation practices for historic textiles such as their display behind glass and in low light conditions to prevent damage and deterioration create significant barriers for visually impaired audiences. Alternative sensory experiences, particularly tactile exploration, have proven essential for increasing engagement with historical and cultural objects for these visitors. Leveraging state-of-the-art machine vision approaches, we present a comprehensive workflow to generate machinable 3D models across multiple materials. We evaluate these reproductions with visually impaired participants using the replica experience framework, analysing preferences across material types, tactile features, and representation techniques. Our preliminary findings demonstrate that combining contextual audio guides with tactile objects significantly enhances understanding and engagement. Notably, providing multiple material versions of the same artefact better accommodates the diverse preferences and tactile sensitivities found within the visually impaired community, suggesting material diversity should be a key consideration in developing inclusive museum experiences.Digital HeritageAccessibility and Inclusive Engagemen