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Riding the Hippogriff: a VR Exploration of Orlando Furioso Epic Poem
No full textThis paper explores the potential of Virtual Reality (VR) in cultural heritage experiences through an interactive reimagining of the journey to the moon in Ludovico Ariosto's Orlando Furioso poem. Developed as part of the ''Furiose Interazioni'' project, officially presented in September 2023, this VR installation, the third of a path structured through a ''station-based'' methodology, aims to bridge historical literature and digital storytelling, engaging users of different ages in a first-person shareable, immersive experience. Starting from the analysis of the state of the art in Virtual Reality for enhancing tangible and intangible cultural heritage, the study discusses the theoretical, technological, and interaction design choices behind the project, addressing challenges such as maintaining narrative authenticity, adapting literary content for interactive media, and ensuring accessibility across diverse audiences. User evaluation results, during the first year of opening, demonstrate how VR enhances engagement, learning, and emotional connection, highlighting the broader implications of immersive technologies in digital heritage.Digital HeritageNarratives, Multimodality, and Emotional Engagement in Heritag
TractMMR: Tractography Streamline Rating through User-guided Matchmaking
No full textDiffusion MRI tractography suffers from an ever-increasing parameter problem in which little to no semantic connection exists between input parameters and output tractogram. We present an approach for users to semantically interact with their data in order to produce a ranking over the parameter space which can be used to filter outputs and in downstream tasks to improve parameter selection. Our approach is a first step in bringing visual analytics closer to daily neuropractice by providing users a direct semantic interaction with their data.EuroVis 2025 - PostersPoster
Material Transforms from Disentangled NeRF Representations
No full textIn this paper, we first propose a novel method for transferring material transformations across different scenes. Building on disentangled Neural Radiance Field (NeRF) representations, our approach learns to map Bidirectional Reflectance Distribution Functions (BRDF) from pairs of scenes observed in varying conditions, such as dry and wet. The learned transformations can then be applied to unseen scenes with similar materials, therefore effectively rendering the transformation learned with an arbitrary level of intensity. Extensive experiments on synthetic scenes and real-world objects validate the effectiveness of our approach, showing that it can learn various transformations such as wetness, painting, coating, etc. Our results highlight not only the versatility of our method but also its potential for practical applications in computer graphics. We publish our method implementation, along with our synthetic/real datasets on https://github.com/astra-vision/BRDFTransformComputer Graphics ForumShady Business: Materials, Textures, and Lighting44
Deep High Dynamic Range Imaging: Reconstruction, Generation and Display
No full textHigh Dynamic Range (HDR) images offer significant advantages over Low Dynamic Range (LDR) images, including greater bit depth, a wider color gamut, and a higher dynamic range. These features not only provide users with an enhanced visual experience but also facilitate post-production processes in photography and filmmaking. Despite the considerable advancements in HDR technology over the years, significant challenges persist in the acquisition and display of HDR content. This thesis systematically explores the potential of leveraging deep learning techniques combined with physical prior knowledge to address these challenges. First, it investigates how implicit neural representations can be utilized to reconstruct all-in-focus HDR images from sparse, defocused LDR inputs, enabling flexible refocusing and re-exposure. Additionally, it extends the scope to the 3D domain by employing 3D Gaussian Splatting to reconstruct HDR all-in-focus fields from multi-view LDR defocused images, supporting novel view synthesis with refocusing and re-exposure capabilities. Expanding further, the thesis investigates strategies for generating HDR content from the in-the-wild LDR data or limited HDR datasets, and subsequently utilizes the resulting HDR generative models as priors to enable the transformation of LDR images into HDR. Finally, it proposes a feature contrast masking loss inspired by visual masking theory, enabling a self-supervised learning tone mapper to display the HDR content on LDR devices.EG Graphics Dissertation Onlin
DigitalTraces: Unveiling Fraud through Interactive User Behaviour Exploration
No full textFraud detection teams in financial institutions face the challenge of identifying suspicious activity within user behaviour. However, existing tools often lack the ability to seamlessly integrate multiple dimensions of digital activity into a single, interactive visualisation, leading to increased cognitive load and preventing analysts from quickly spotting anomalies in varying sources of information. This paper introduces DigitalTraces, a visual analytics tool aimed at improving the detection of fraudulent patterns particularly in the dimensions tied with digital activity. The system combines several stacked timelines to offer an overview of multiple activity dimensions, integrating online banking session data, device identifiers, transactional activities, and account information. We validated our tool with a think-aloud experiment where two fraud analysts were tasked with detecting anomalies in a financial fraud scenario. Experts emphasised the tool's ability to provide intuitive insights and enhance understanding.EuroVis 2025 - Short PapersSystems and Application
Immersive RockArt: When rock carvings meet photogrammetry and computer graphics
No full textRock Art Immersive is an interdisciplinary project for the 3D documentation, valorisation, communication, and tourist promotion of the UNESCO site of the Pitoti rock carvings in Val Camonica (Italy). Photogrammetry and Computer Graphics are coupled to create reality-based interactive and communicative material to safeguard and valorize a heritage site.Digital HeritageDigitization Case Studie
Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference: Frontmatter
No full textSmart Tools and Applications in Graphics - Eurographics Italian Chapter Conferenc
FAHNet: Accurate and Robust Normal Estimation for Point Clouds via Frequency-Aware Hierarchical Geometry
No full textPoint cloud normal estimation underpins many 3D vision and graphics applications. Precise normal estimation in regions of sharp curvature and high-frequency variation remains a major bottleneck; existing learning-based methods still struggle to isolate fine geometry details under noise and uneven sampling. We present FAHNet, a novel frequency-aware hierarchical network that precisely tackles those challenges. Our Frequency-Aware Hierarchical Geometry (FAHG) feature extraction module selectively amplifies and merges cross-scale cues, ensuring that both fine-grained local features and sharp structures are faithfully represented. Crucially, a dedicated Frequency-Aware geometry enhancement (FA) branch intensifies sensitivity to abrupt normal transitions and sharp features, preventing the common over-smoothing limitation. Extensive experiments on synthetic benchmarks (PCPNet, FamousShape) and real-world scans (SceneNN) demonstrate that FAHNet outperforms state-of-the-art approaches in normal estimation accuracy. Ablation studies further quantify the contribution of each component, and downstream surface reconstruction results validate the practical impact of our design.Computer Graphics ForumCreating and Processing Point Clouds44
The Effect of Internal Patterns on Perception Accuracy in Bar Charts
No full textBar charts are widely used for comparing categorical data due to their simplicity and effectiveness, while pictographs enhance engagement and memory retention. This study aims to combine the strengths of both visualization techniques through redundant encoding, integrating internal patterns to improve perception accuracy. By testing various pattern designs, the study evaluates their impact on value estimation. Results indicate that some patterns enhance accuracy, while others introduce complexity that can hinder readability. These findings contribute to optimizing bar chart design for clearer and more intuitive data interpretation.EuroVis 2025 - Short PapersEmpirical and Perception Studie