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Projecting BRDF Materials using Polarimetric Normal Estimation
No full textThis study proposes a method based on Spatial Augmented Reality (SAR) technology to transform the perceived material of an object's surface by projecting imagery rendered from measured BRDF data. The core of this method lies in the non-contact estimation of the object's three-dimensional surface shape from images captured at four different polarization angles. Specifically, Stokes parameters are calculated from four polarized images, and a normal vector is derived for each pixel based on the resulting Angle of Linear Polarization (AoLP) and Degree of Linear Polarization (DoLP). Next, rendering is performed using the estimated normal map and a measured BRDF database. The resulting appearance of the target material is then projected onto the object via a projector-camera system. Experiments demonstrate the ability to change the material appearance of certain objects, such as plaster statues and plastic products. The significance of this research lies in demonstrating a new framework for presenting material appearance by combining shape estimation using polarization with physically based rendering.ICAT-EGVE 2025 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual EnvironmentsRendering and Sensin
A Texture‐Free Practical Model for Realistic Surface‐Based Rendering of Woven Fabrics
No full textRendering woven fabrics is challenging due to the complex micro geometry and anisotropy appearance. Conventional solutions either fully model every yarn/ply/fibre for high fidelity at a high computational cost, or ignore details, that produce non‐realistic close‐up renderings. In this paper, we introduce a model that shares the advantages of both. Our model requires only binary patterns as input yet offers all the necessary micro‐level details by adding the yarn/ply/fibre implicitly. Moreover, we design a double‐layer representation to handle light transmission accurately and use a constant timed () approach to accurately and efficiently depict parallax and shadowing‐masking effects in a tandem way. We compare our model with curve‐based and surface‐based, on different patterns, under different lighting and evaluate with photographs to ensure capturing the aforementioned realistic effects.Computer Graphics ForumOriginal Article44
ChainPro - A Web3 Platform for Artists
No full textBlockchain Technology and Web3 solutions act as catalysts for innovation and creativity, providing artists with tools to experiment with new business models, services, and applications, free from the constraints of centralized entities. Digital innovation significantly impacts artists' ability to connect and create trusted communities, leveraging these technologies. Several platforms support the digitalization of creative arts, but Web3 platforms can revolutionize the sector by simplifying blockchain adoption, enabling the formation of Decentralized Autonomous Organization (DAOs), fostering innovative business models, enhancing legal transparency, nurturing communities, and driving creativity and competitiveness. This paper explores how Blockchain, Non-Fungible Tokens (NFTs), and related Web3 technologies can empower Creators by providing greater ownership and control over their creative works. A state-of-the-art analysis identifies the technical requirements for integrating blockchain and NFT-based services for user engagement and innovative business models. Additionally, a user-centred design approach was adopted to collect user requirements for managing creative works ownership, Intellectual Property Rights and revenue distribution through smart contracts. The results led to the implementation and validation of ChainPro for Artists (ChainPro4Artists), a Web3 innovative platform that supports advanced blockchain technologies, including Layer 2 infrastructures (e.g., Polygon) and DAOs tailored for artist communities.Digital HeritageInfrastructures, Platforms and Digital Ecosystem
PartFull: A Hybrid Method for Part-Aware 3D Object Reconstruction from Sparse Views
No full textRecent advancements in 3D object reconstruction have been significantly enhanced by generative models; however, challenges remain when detailed 3D shapes are reconstructed from limited, sparse views. Traditional methods often require multiple input views and known camera poses, whereas newer approaches that leverage diffusion models from single images encounter realworld data limitations. In response, we propose ''PartFull'', a novel framework for part-aware 3D reconstruction using a hybrid approach. ''PartFull'' generates realistic 3D models from sparse RGB images by combining implicit and explicit representations to optimize surface reconstruction. Starting with sketch-based 3D models from individual views, these models are fused into a coherent object. Our pipeline incorporates a pretrained latent space for part-aware implicit representations and a deformable grid for feature volume construction and surface optimization. PartFull's joint optimization of surface geometry, topology, and implicit part segmentation constitutes a new approach to addressing the challenges of 3D reconstruction from sparse views.Eurographics 2025 - Short PapersShort Paper
The IlluminAI project: a deep neural network and immersive visualization system to enhance illuminated manuscripts
No full textIn museum and archive digital catalogues, illuminated manuscript pages can often be found within heterogeneous groups of reproductions, coexisting with other types of artworks and objects. With them, figurative miniatures share the depicted subjects that are recognizable, regardless of the medium used, for their specific iconography. Thanks to the use of a visual vocabulary still common today, illuminations are also the element that mostly attracts the non-academic public, making the often-incomprehensible content partly accessible despite the language. The paper will present IlluminAI, a project still in progress, which aims at the enhancement of late medieval and Renaissance illuminated codices using artificial intelligence through an immersive visualization system capable of automatically recognizing manuscript sheets, analyzing their content, and relating specimens with similar illustrations or artworks from the same theme. After some brief references to contextualize the work, we will expose the first completed phase of the research focusing on the original dataset composition before outlining the chosen semi-automatic labeling strategy and the interactive machine learning approach. This was used to create with transfer learning a model able to recognize manuscript pages and identify inside of them five characteristic layout elements. We will then switch to the second ongoing part of the project with the design of the immersive Web3D system, based on the open-source ATON framework, that will give users the possibility to explore, inspect, compare and query large amounts of images in a three-dimensional space. The data aggregation criteria and the presentation modes will be described with particular attention to the spatial organization and novel 3D interfaces.Digital HeritageExtracting Knowledge from Digitized Asset
WaterGS: Physically-Based Imaging in Gaussian Splatting for Underwater Scene Reconstruction
No full textReconstructing underwater object geometry from multi-view images is a long-standing challenge in computer graphics, primarily due to image degradation caused by underwater scattering, blur, and color shift. These degradations severely impair feature extraction and multi-view consistency. Existing methods typically rely on pre-trained image enhancement models as a preprocessing step, but often struggle with robustness under varying water conditions. To overcome these limitations, we propose WaterGS, a novel framework for underwater surface reconstruction that jointly recovers accurate 3D geometry and restores true object colors. The core of our approach lies in introducing a Physically-Based imaging model into the rendering process of 2D Gaussian Splatting. This enables accurate separation of true object colors from water-induced distortions, thereby facilitating more robust photometric alignment and denser geometric reconstruction across views. Building upon this improved photometric consistency, we further introduce a Gaussian bundle adjustment scheme guided by our physical model to jointly optimize camera poses and geometry, enhancing reconstruction accuracy. Extensive experiments on synthetic and real-world datasets show that WaterGS achieves robust, high-fidelity reconstruction directly from raw underwater images, outperforming prior approaches in both geometric accuracy and visual consistency.Computer Graphics ForumGaussian Splatting44
From Cluster to Desktop: A Cache-Accelerated INR framework for Interactive Visualization of Tera-Scale Data
No full textMachine learning has enabled the use of implicit neural representations (INRs) to efficiently compress and reconstruct massive scientific datasets. However, despite advances in fast INR rendering algorithms, INR-based rendering remains computationally expensive, as computing data values from an INR is significantly slower than reading them from GPU memory. This bottleneck currently restricts interactive INR visualization to professional workstations. To address this challenge, we introduce an INR rendering framework accelerated by a scalable, multi-resolution GPU cache capable of efficiently representing tera-scale datasets. By minimizing redundant data queries and prioritizing novel volume regions, our method reduces the number of INR computations per frame, achieving an average 5× speedup over the state-of-the-art INR rendering method while still maintaining high visualization quality. Coupled with existing hardware-accelerated INR compressors, our framework enables scientists to generate and compress massive datasets in situ on high-performance computing platforms and then interactively explore them on consumer-grade hardware post hoc.Eurographics Symposium on Parallel Graphics and VisualizationPaper
Non-linear, Team-based VR Training for Cardiac Arrest Care with enhanced CRM Toolkit
No full textThis paper introduces iREACT, a novel VR simulation addressing key limitations in traditional cardiac arrest (CA) training. Conventional methods struggle to replicate the dynamic nature of real CA events, hindering Crew Resource Management (CRM) skill development. iREACT provides a non-linear, collaborative environment where teams respond to changing patient states, mirroring real CA complexities. By capturing multi-modal data (user actions, cognitive load, visual gaze) and offering real-time and post-session feedback, iREACT enhances CRM assessment beyond traditional methods. A formative evaluation with medical experts underscores its usability and educational value, with potential applications in other high-stakes training scenarios to improve teamwork, communication, and decision-making.Eurographics 2025 - Short PapersShort Paper
Rediscovering Mural Paintings: Experiencing Medieval Art as Originally Conceived Through Historical Light Simulation
No full textThe lighting of Cultural Heritage artifacts plays a crucial role in how we perceive and consequently understand artworks. However, lighting is typically designed to enhance the experience of contemporary visitors, often diverging significantly from the original conditions and techniques under which these works were created. This disconnect between historical and modern lighting conditions makes it difficult to fully understand the original visual experience. This issue is particularly evident in the case of Romanesque paintings, which are now exhibited in well-lit museums, but were originally displayed in dimly lit churches. In this paper, we present a method to bridge this gap, focusing on the paintings of a Romanesque church. We achieve this by simulating the original lighting conditions. Our approach encompasses the entire pipeline, from acquiring data of equivalent historical light sources and computing natural lighting to performing physically based rendering for accurate light simulation. Additionally, we have developed a web application that allows users to inspect and compare the resulting HDR images using different tone mapping and luminance operators. Our work provides a valuable tool for art historians and the general public to explore different lighting hypotheses and gain a deeper understanding of the experience of visiting a medieval church as originally conceived.Digital HeritageReconstructing the Pas
Temporal Brightness Management for Immersive Content
No full textModern virtual reality headsets demand significant computational resources to render high-resolution content in real-time. Therefore, prioritizing power efficiency becomes crucial, particularly for portable versions reliant on batteries. A significant portion of the energy consumed by these systems is attributed to their displays. Dimming the screen can save a considerable amount of energy; however, it may also result in a loss of visible details and contrast in the displayed content. While contrast may be partially restored by applying post-processing contrast enhancement steps, our work is orthogonal to these approaches, and focuses on optimal temporal modulation of screen brightness. We propose a technique that modulates brightness over time while minimizing the potential loss of visible details and avoiding noticeable temporal instability. Given a predetermined power budget and a video sequence, we achieve this by measuring contrast loss through band decomposition of the luminance image and optimizing the brightness level of each frame offline to ensure uniform temporal contrast loss. We evaluate our method through a series of subjective experiments and an ablation study, on a variety of content. We showcase its power-saving capabilities in practice using a built-in hardware proxy. Finally, we present an online version of our approach which further emphasizes the potential for low level vision models to be leveraged in power saving settings to preserve content quality.Eurographics Symposium on RenderingLight and Brightnes