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17121 research outputs found
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Fast Sphere Tracing of Procedural Volumetric Noise for very Large and Detailed Scenes
No full textReal-time walk through very large and detailed scenes is a challenge for both content design, data management, and rendering, and requires LOD to handle the scale range. In the case of partly stochastic content (clouds, cosmic dust, fire, terrains, etc.), proceduralism allows arbitrary large and detailed scenes with no or little storage and offers embedded LOD, but the rendering gets even costlier. In this paper, we propose to boost the performance of Fractional Brownian Motion (FBM)-based noise rendering (e.g., 3D Perlin noise, hypertextures) in two ways: improving the stepping efficiency of Sphere Tracing of general Signed Distance Functions (SDF) considering the first and second derivatives, and treating cascaded sums such as FBM as nested bounding volumes. We illustrate this on various scenes made of either opaque material, constant semi-transparent material, or non-constant (i.e., full volumetric inside) material, including animated content - thanks to on-the-fly proceduralism. We obtain real-time performances with speedups up to 12-folds on opaque or constant semi-transparent scenes compared to classical Sphere tracing, and up to 2-folds (through empty space skipping optimization) on non-constant density volumetric scenes.Computer Graphics ForumEclipsing the Ordinary in Visualization44
Cards, Charts, and Strategy: A Game-Based Approach to Data Visualization for Pattern
No full textIn today's society, the ability to read and interpret data visualizations has become a critical skill in both professional and academic contexts. Therefore, fostering visualization literacy from an early stage is essential in teaching students to understand and interpret different types data visualizations. Research highlights gamification as an effective method for enhancing visualization literacy by promoting active learning and motivating learners of all ages. In this context, a card game has been developed to challenge players to identify the most appropriate visualization type for different given datasets. This educational card game aims to deepen understanding and practical application of data visualization concepts while maintaining an engaging and interactive experience for learners.EuroVis Workshop on Visualization Play, Games, and ActivitiesPaper
All-frequency Full-body Human Image Relighting
No full textRelighting of human images enables post-photography editing of lighting effects in portraits. The current mainstream approach uses neural networks to approximate lighting effects without explicitly accounting for the principle of physical shading. As a result, it often has difficulty representing high-frequency shadows and shading. In this paper, we propose a two-stage relighting method that can reproduce physically-based shadows and shading from low to high frequencies. The key idea is to approximate an environment light source with a set of a fixed number of area light sources. The first stage employs supervised inverse rendering from a single image using neural networks and calculates physically-based shading. The second stage then calculates shadow for each area light and sums up to render the final image. We propose to make soft shadow mapping differentiable for the area-light approximation of environment lighting. We demonstrate that our method can plausibly reproduce all-frequency shadows and shading caused by environment illumination, which have been difficult to reproduce using existing methods.Computer Graphics ForumShady Business: Materials, Textures, and Lighting44
In Situ Workload Estimation for Block Assignment and Duplication in Parallelization-Over-Data Particle Advection
No full textParticle advection is a foundational algorithm for analyzing a flow field. The commonly used Parallelization-Over-Data (POD) strategy for particle advection can become slow and inefficient when there are unbalanced workloads, which are particularly prevalent in in situ workflows. In this work, we present an in situ workflow containing workload estimation for block assignment and duplication in a parallelization-over-data algorithm. With tightly coupled workload estimation and load-balanced block assignment strategy, our workflow offers a considerable improvement over the traditional round-robin block assignment strategy. Our experiments demonstrate that particle advection is up to 3X faster and associated workflow saves approximately 30% of execution time after adopting strategies presented in this work.Computer Graphics ForumFlow Vi
Integrated survey for heritage digitisation. The case study of Venaria Reale within HERITALISE project
No full textThe documentation and digitization of Cultural Heritage (CH) assets are fundamental for their conservation, monitoring, and long-term accessibility. HERITALISE is a research project aimed at developing a cloud-based, interoperable ecosystem aligned with the European Collaborative Cloud for Cultural Heritage (ECCCH), enabling the structured acquisition, management, and sharing of heterogeneous CH data. The project, currently at its earliest stage, applies advanced geomatics techniques - including Terrestrial Laser Scanning (TLS), SLAM-based mobile mapping, and UAV photogrammetry - in a multiscale framework for the 3D digitization of the UNESCO site Reggia di Venaria Reale (Italy). Particular focus is given to the Great Gallery and the St. Uberto Church, where high-resolution metric data are integrated with environmental and microclimatic monitoring, material analyses, and historical documentation to support diagnostics, maintenance, and interpretation. The approach extends to landscape heritage and movable assets, such as the Gardens of Fluid Sculptures and 18th-century wooden furniture, combining 3D documentation, tomography, and digital fabrication. HERITALISE proposes a scientific methodology for bridging traditional and digital CH practices, fostering sustainable and interdisciplinary preservation strategies.Digital HeritagePoster
Integrating Layer-Wise Relevance Propagation with Stable Diffusion for Enhanced Interpretability
No full textDiffusion-based generative models, such as Stable Diffusion and DALL-E, have revolutionized artificial intelligence by enabling high-quality image generation from textual descriptions. Despite their success, these models raise ethical concerns, such as style appropriation and misuse, closely tied to the interpretability and transparency of the underlying mechanisms. This paper introduces a framework integrating Layer-wise Relevance Propagation (LRP) into the Stable Diffusion model to enhance interpretability. LRP assigns relevance scores to specific elements of textual prompts, allowing users to understand and visualize how input text influences image generation. We also present an interactive web-based visualization tool that supports intuitive exploration of diffusion processes. By improving interpretability, this approach fosters responsible use of generative AI technologies. A user study involving 35 participants demonstrates the tool's accessibility and effectiveness.EuroVis Workshop on Visual Analytics (EuroVA)Visual Analytics Applications and System
Mint: Discretely Integrable Moments for Symmetric Frame Fields
No full textThis paper studies the problem of unconstrained (e.g. not orthogonal or unit) symmetric frame field design in volumes. Our principal contribution is a novel (and theoretically well-founded) local integrability condition for frame fields represented as a triplet of symmetric tensors of second, fourth, and sixth order. We also formulate a novel smoothness energy for this representation. To validate our discritization, we study the problem of seamless parameterization of volumetric objects. We compare against baseline approaches by formulating a smooth, integrable, and approximately octahedral frame objective in our discritization. Our method is the first to solve these problems with automatic placement of singularities while also enforcing a symmetric proxy for local integrability as a hard constraint, achieving significantly higher quality parameterizations, in expectation, relative to other frame field design based approaches.Computer Graphics ForumFields on Meshes44
VECNA: Visual Exploration, Comparison and Analysis of Reconstructed Spatiotemporal Scientific Simulation Data
No full textData-driven sampling and reconstruction techniques are increasingly being employed in scientific computing applications to aggressively reduce data volumes while retaining the crucial features of spatiotemporal datasets. Such data must be reconstructed for analysis, but it is difficult for domain experts to assess reconstruction quality, particularly given the pace at which new methods are being developed and a lack of support in existing tools. To help address this, we introduce VECNA, a visual analytics system for exploring and comparing reconstructed scientific datasets. Developed through collaboration with high-performance computing researchers, VECNA enables intuitive qualitative and quantitative comparisons among diverse reconstruction methodologies. We validate VECNA via a usage scenario and empirical expert assessments to demonstrate its efficacy in empowering users to discern nuances in reconstruction quality and identify regions of interest within datasets, facilitating more informed subsequent analyses.EuroVis 2025 - Short PapersSystems and Application
Integration of Kompakkt into a Virtual Reality CAVE Environment: The CAVE-Kompakkt-Viewer
No full textThis poster presents the integration of the Kompakkt platform into the University of Cologne's CAVE (Cave Automatic Virtual Environment), forming the CAVE-Kompakkt Viewer as part of the broader "Virtual Campus" initiative. Kompakkt is a web-based tool developed for the exploration and annotation of 3D cultural heritage objects, supporting rich media content and FAIR data principles. The University's CAVE system provides the spatial framework for interactive visualization and collaborative research. The CAVE-Kompakkt Viewer serves as a bridge between web-based cultural heritage resources and immersive virtual reality experiences. It enables users to dynamically load, explore, and interact with 3D models in real time, offering an intuitive and high-fidelity environment for research and education. The system transforms the act of viewing into a spatial and embodied experience, enhancing understanding and engagement with digital heritage data.Digital HeritagePoster