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Reflections on Teaching Data-Driven Storytelling at the Journalism School
No full textThe integration of data visualization in journalism has catalyzed the growth of data storytelling in recent years. Today, it is increasingly common for journalism schools to incorporate data visualization into their curricula. However, the approach to teaching data visualization in journalism schools can diverge significantly from that in computer science or design schools, influenced by the varied backgrounds of students and the distinct value systems inherent to these disciplines. This paper reviews my experience and reflections on teaching data-driven storytelling in a journalism school in Shanghai, China. To begin with, I discuss three prominent characteristics of journalism education (i.e., students' lack of quantitative literacy, the tension between humanism and technocentrism, and the high requirements for content professionalism) that pose challenges for course design and teaching. Then, for each challenge, I share firsthand teaching experiences and discuss corresponding approaches for teaching, such as trying to put visualization into a news context and finding commonality between data-driven storytelling and traditional storytelling. Overall, this paper aims to provide reference and inspiration for instructors who are teaching data visualization and data-driven storytelling to students with non-technical backgrounds.EuroVis 2025 - Education PapersEducation Papers Session
Stochastic Ray Tracing of Transparent 3D Gaussians
No full text3D Gaussian splatting has been widely adopted as a 3D representation for novel-view synthesis, relighting, and 3D generation tasks. It delivers realistic and detailed results through a collection of explicit 3D Gaussian primitives, each carrying opacity and view-dependent color. However, efficient rendering of many transparent primitives remains a significant challenge. Existing approaches either rasterize the Gaussians with approximate per-view sorting or rely on high-end RTX GPUs. This paper proposes a stochastic ray-tracing method to render 3D clouds of transparent primitives. Instead of processing all ray-Gaussian intersections in sequential order, each ray traverses the acceleration structure only once, randomly accepting and shading a single intersection (or N intersections, using a simple extension). This approach minimizes shading time and avoids primitive sorting along the ray, thereby minimizing register usage and maximizing parallelism even on low-end GPUs. The cost of rays through the Gaussian asset is comparable to that of standard mesh-intersection rays. The shading is unbiased and has low variance, as our stochastic acceptance achieves importance sampling based on accumulated weight. The alignment with Monte Carlo philosophy simplifies implementation and integration into a conventional path-tracing framework.Eurographics Symposium on RenderingGaussian
4D Data Organization and Alignment atWorld Scale
No full textA current challenge related to 4D viewers at world scale is the low number and geographic and temporal coverage of 3D models available. To overcome this issue, we work on different tools to (1) harvest 3D models, images, and location-based information from multiple sources via automated and community-based approaches; (2) automatically process these into 4D cityscape models; (3) make the 4D models accessible in two visualizations: via a 4D browser and a location-dependent augmented reality representation. Since previous articles report on the pipeline, tools, and usage scenarios, this contribution highlights ongoing developments in (1) large-scale 3D data retrieval; (2) 3D mesh model and LiDAR processing; and (3) novel view synthesis via Gaussian Splatting. The datasets are viewed in two 4D world viewers for (4) desktop browsing and (5) location-based mobile use. To date, they have been tested in seven countries.Digital HeritageAnalysing and Documenting the Creation Process, Evolution and Contex
Region-Adaptive Low-Light Image Enhancement with Light Effect Suppression and Detail Preservation
No full textLow-light image enhancement seeks to improve the visual quality of images captured under poor illumination, yet existing methods often struggle with unnatural artifacts, overexposure, or detail loss, particularly in challenging real-world scenarios like underground coal mines. We propose a novel unsupervised region-adaptive framework that integrates light effect suppression and detail preservation to address these issues. Leveraging Retinex theory, our approach decomposes images into illumination and reflectance components, employing a region segmentation module to distinguish dark and bright areas for targeted enhancement. A lightweight denoising network mitigates noise, while an adaptive illumination enhancer and light effect suppressor collaboratively optimize illumination to ensure natural appearance and correct visual imbalances. A composite loss function balances brightness enhancement, structural integrity, and artifact suppression across regions. Extensive experiments on the LOL-v2, LSRW and our private datasets demonstrate superior performance. For instance, on our dataset, improvements of 3.26% in BRISQUE, 0.24% in NIQE, and 11.22% in PIQE were achieved compared to state-of-the-art methods, providing visually pleasing results with enhanced brightness, reduced artifacts, and preserved textures, making it well-suited for real-world applications.Pacific Graphics Conference Papers, Posters, and DemosEnhancing Image
Learn to Build Your Own Museum in the Metaverse
No full textThe Museums in the Metaverse (MiM) platform has been developed by the University of Glasgow and Edify with funding from Innovate UK. The purpose of the platform is enable users to build and publish their own virtual museums without the need to code or have other specialist technical knowledge. This tutorial will lead attendees through the process of doing just that. We will showcase the capabilities of the platform, then guide users through asset identification, upload, and placement, then through experience curation and publication. The output of the tutorial will be draft virtual museum experiences that attendees can publicly publish at a later date, if they so choose.Digital HeritageTutorial
Importance Sampling of the Micrograin Visible NDF
No full textImportance sampling of visible normal distribution functions (vNDF) is a required ingredient for the efficient rendering of microfacet-based materials. In this paper, we explain how to sample the vNDF for the micrograin material model [LRPB23], which has been recently improved to handle height-normal correlations through a new Geometric Attenuation Factor (GAF) [LRPB24], leading to a stronger impact on appearance compared to the earlier Smith approximation. To this end, we make two contributions: we derive analytic expressions for the marginal and conditional cumulative distribution functions (CDFs) of the vNDF; we provide efficient methods for inverting these CDFs based respectively on a 2D lookup table and on the triangle-cut method [Hei20].Computer Graphics ForumSampling and Guiding44
Digging through the Virtual Sand of Time: Development and Evaluation of Hetepheres Tomb VR
No full textLess than 100 meters from the base of the Great Pyramid of Giza lies the tomb of Queen Hetepheres I. 2025 marks the 100th anniversary of its discovery, a milestone that is likely to draw renewed attention to this relatively unknown archaeological site. To share the story of the tomb's excavation and the mysteries surrounding it with a broader audience, we developed the virtual reality experience Hetepheres Tomb VR. In this paper, we present the didactic concept behind the experience, along with practical implementation strategies designed to ensure maximum user-friendliness and minimize motion sickness. To evaluate the effectiveness of these design choices, we conducted a user study, the results of which are discussed in detail. These findings also informed subsequent optimizations of the application. With this work, we aim to provide museum-related VR projects - particularly those in the conceptual phase - with insights into our development process. We hope to offer both guidance and inspiration for creating engaging, accessible virtual experiences. Hetepheres Tomb VR is currently available for free on Steam and the Meta Quest app store, making it suitable for use in educational settings and museum exhibitions.Digital HeritageModern Technologies for Serious Gaming in Cultural Heritag
Artificial Intelligence and Cultural Heritage in Practice: exploring approaches to operationalising values, law, and responsible openness
No full textThis panel explores the intersection of Artificial Intelligence and cultural heritage, drawing on interdisciplinary insights from legal experts, digital humanists, and cultural professionals. Framed around values-based practice, legal compliance, and responsible openness, it addresses urgent questions around AI policy, regulatory uncertainty, ethical data reuse, and sector-specific strategies. Grounded in current research and practice, the session seeks to foster critical, cross-sector dialogue and inform responsible AI adoption in the cultural heritage field.Digital HeritagePanels, Roundtable
VortexTransformer: End-to-End Objective Vortex Detection in 2D Unsteady Flow Using Transformers
No full textVortex structures play a pivotal role in understanding complex fluid dynamics, yet defining them rigorously remains challenging. One hard criterion is that a vortex detector must be objective, i.e., it needs to be indifferent to reference frame transformations. We propose VortexTransformer, a novel deep learning approach using point transformer architectures to directly extract vortex structures from pathlines. Unlike traditional methods that rely on grid-based velocity fields in the Eulerian frame, our approach operates entirely on a Lagrangian representation of the flow field (i.e., pathlines), enabling objective identification of both strong and weak vortex structures. To train VortexTransformer, we generate a large synthetic dataset using parametric flow models to simulate diverse vortex configurations, ensuring a robust ground truth. We compare our method against CNN and UNet architectures, applying the trained models to real-world flow datasets. VortexTransformer is an end-to-end detector, which means that reference frame transformations as well as vortex detection are handled implicitly by the network, demonstrating the ability to extract vortex boundaries without the need for parameters such as arbitrary thresholds, or an explicit definition of a vortex. Our method offers a new approach to determining objective vortex labels by using the objective pairwise distances of material points for vortex detection and is adaptable to various flow conditions.Computer Graphics ForumEclipsing the Ordinary in Visualization44
A Method for Optimizing the Rendering Order of Scatterplots
No full textRendering order is crucial for generating effective scatterplots, as a rendering sequence can cause anomalous data points to be obscured by others. This issue is particularly significant in the field of explainable artificial intelligence (XAI), where large volumes of data can prevent users from observing misclassified instances. This poster introduces a novel method for sorting data points and rendering them sequentially to reduce the likelihood of anomalous points being hidden. First, we normalize the two coordinates of the scatterplots to mitigate the impact of differing value ranges. Next, we propose a method for calculating the anomaly index of each data point. Finally, we sort the data points based on their anomaly index and render them sequentially.We compare our method with existing approaches on scatterplots generated by dimensionality reduction (DR) techniques applied to a pretrained convolutional neural network (CNN) trained on the MNIST dataset. The results demonstrate that our method enables easier identification of misclassified (anomalous) data points compared to category-based and random rendering orders.EuroVis 2025 - PostersPoster