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Adaptive Multiple Control Variates for Many-Light Rendering
No full textMonte Carlo integration estimates the path integral in light transport by randomly sampling light paths and averaging their contributions. However, in scenes with many lights, the resulting estimates suffer from noise and slow convergence due to highfrequency discontinuities introduced by complex light visibility, scattering functions, and emissive properties. To mitigate these challenges, control variates have been employed to approximate the integrand and reduce variance. While previous approaches have shown promise in direct illumination application, they struggle to efficiently handle the discontinuities inherent in manylight environments, especially when relying on a single control variate. In this work, we introduce an adaptive method that generates multiple control variates tailored to the spatial distribution and number of lights in the scene. Drawing inspiration from hierarchical light clustering methods like Lightcuts, our approach dynamically determines the number of control variates. We validate our method on the direct illumination problem in scenes with many lights, demonstrating that our adaptive multiple control variates not only outperform single control variate strategy but also achieve a modest improvement over current stateof- the-art many-light sampling techniques.Eurographics Symposium on RenderingLight and Brightnes
Playing with Knowledge: Leveraging Visualization Games for Data Validation and Inspiration
No full textWe present an approach to use visualization games for data validation and inspiration in a collaborative coding context. As part of an interactive coding system that lets coders create a tag hierarchy and tag data items, we designed multiple games that support validating that data and exploring it in a novel way. Each game has mechanics inspired by existing games and incorporates visualization and externalization to varying degrees. By playing these games, coders randomly sample the data space to pinpoint problems and find inspiration, like discovering gaps in the data or contemplating novel item-tag combinations. Game results are automatically tracked to let coders analyze their performance and find out in which cases they tend to make mistakes. Coders can also create objection notes at the end of a game to externalize insights which are accessible in other parts of the system. For example, if a coder is convinced that an item should not have a specific tag they were shown in a game, they can create an objection about this issue that all system users can see. Our games can be played with different datasets at https://arielmant0.github.io/collacode/?tab=games.EuroVis Workshop on Visualization Play, Games, and ActivitiesPaper
VF-Plan: Bridging the Art Gallery Problem and Static LiDAR Scanning with Visibility Field Optimization
No full textViewpoint planning is critical for efficient 3D data acquisition in applications such as 3D reconstruction, building life-cycle management, navigation, and interior decoration. However, existing methods often neglect key optimization objectives specific to static LiDAR systems, resulting in redundant or disconnected viewpoint networks. The viewpoint planning problem (VPP) extends the classical Art Gallery Problem (AGP) by requiring full coverage, strong registrability, and coherent network connectivity under constrained sensor capabilities. To address these challenges, we introduce a novel Visibility Field (VF) that accurately captures the directional and range-dependent visibility properties of static LiDAR scanners. We further observe that visibility information naturally converges onto a 1D skeleton embedded in the 2D space, enabling significant searching space reduction. Leveraging these insights, we develop a greedy optimization algorithm tailored to the VPP, which constructs a minimal yet fully connected Viewpoint Network (VPN) with low redundancy. Experimental evaluations across diverse indoor and outdoor scenarios confirm the scalability and robustness of our method. Compared to expert-designed VPNs and existing state-of-the-art approaches, our algorithm achieves comparable or fewer viewpoints while significantly enhancing connectivity. In particular, it reduces the weighted average path length by approximately 95%, demonstrating substantial improvements in compactness and structural efficiency. Code is available at https://github.com/xiongbiaostar/VFPlan.Pacific Graphics Conference Papers, Posters, and Demos3D Reconstructio
Detail-Preserving Real-Time Hair Strand Linking and Filtering
No full textRealistic hair rendering remains a significant challenge in computer graphics due to the intricate microstructure of hair fibers and their anisotropic scattering properties, which make them highly sensitive to noise. Although recent advancements in imagespace and 3D-space denoising and antialiasing techniques have facilitated real-time rendering in simple scenes, existing methods still struggle with excessive blurring and artifacts, particularly in fine hair details such as flyaway strands. These issues arise because current techniques often fail to preserve sub-pixel continuity and lack directional sensitivity in the filtering process. To address these limitations, we introduce a novel real-time hair filtering technique that effectively reconstructs fine fiber details while suppressing noise. Our method improves visual quality by maintaining strand-level details and ensuring computational efficiency, making it well-suited for real-time applications in video games and virtual reality (VR) and augmented reality (AR) environments.Computer Graphics ForumReal-Time Rendering44
EuroVa 2025: Frontmatter
No full textEuroVis Workshop on Visual Analytics (EuroVA)EuroVis Workshop on Visual Analytics (EuroVA) 202
From Scanned Pages to Semantic Graphs: Scalable Methods for Extracting Historical and Cultural Knowledge Across Heterogeneous Texts
No full textWe present a multilayered methodology for processing digitized historical texts, enabling cross-relational analysis across time periods, languages, and subject domains. Drawing from multiple DH platforms (Tsadikim, Two Enlightenments, Corporeality), we demonstrate an integrated pipeline combining adaptive OCR, noise-tolerant keyword extraction, and NER. Custom preprocessing and fuzzy matching techniques allow for meaningful text recovery from degraded scans in Polish, German, and Yiddish. Data are enriched with spatial and temporal metadata, indexed by topic and linked across projects. The resulting datasets support trend analysis, social network modeling, and discourse mapping. Our approach enables researchers to trace linguistic shifts and intellectual networks over centuries without manual review of source pages. This workflow facilitates interoperable exploration of cultural data and demonstrates how machine learning can assist in recovering semantic relationships from fragmented historical records. The methodology was tested on Enlightenment-era and early 20th-century journals, revealing both technical challenges and insights into evolving ideological, medical, and theological vocabularies.Digital HeritageExtracting Knowledge from Digitized Asset
Broadening Data and Digital Skills within Communities to Access Digital Research Infrastructures
No full textThis paper advances understanding on how computational and data-driven research methods can be expanded within the Arts and Humanities community, including researchers and practitioners in the Cultural Heritage sector. In particular, the research investigates the catalysts and motivators to enhance the digital and data literacy of researchers and practitioners and recommendations for longer term adoption of these skills. The research is contextualised within current efforts to expand access to national and European Digital Research Infrastructures. It is conducted through a series of scaffolded learning interventions implemented through a training initiative in the United Kingdom (UK). The paper describes this training initiative and the evaluation of the effectiveness of these interventions employing a mixed data approach. The research concludes with a set of recommendations for designing training programmes amongst learning communities, including using skills curricula based on domain-specific data processes and infrastructures as well as active learning approaches.Digital HeritageStandards and Community-Driven Tool
Bijective Feature-Aware Contour Matching
No full textComputing maps between data sequences is a fundamental problem with various applications in the fields of geometry and signal processing. As such, a multitude of approaches exist, that make trade-offs between flexibility, performance, and accuracy. Even recent approaches cannot be applied to periodic data, such as contours, without significant compromises due to their map representation or method of optimization. We propose a universal method to optimize maps between periodic and non periodic univariate sequences. By continuously optimizing a piecewise linear approximation of the smooth map on a common intermediate domain, we decouple the map and input resolution. Our optimization offers bijectivity guarantees and flexibility with regards to applications and data modality. To robustly converge towards a high quality solution we initially apply a lowpass filter to the input. This creates a scale space that suppresses local features in the early phase of the optimization (global phase) and gradually adds them back later (local phase). We demonstrate the versatility of our method on various scenarios with different types of sequences, including multi-contour morphing, signature prototypes, symmetry detection, and 3D motioncapture- data alignment.Vision, Modeling, and VisualizationGeometry, Simulation, and Optimizatio
Issue Information
No full textInformation page for issue 44(6) of Computer Graphics Forum, published in September 2025.Computer Graphics ForumIssue Information44
3D Curve Development with Crossing and Twisting from 2D Drawings
No full textDesigning 3D curves with specified crossings and twistings often requires tedious view adjustments. We present a 3D curve development from 2D drawing with controlled crossings and twistings. We introduce a two-strand 2D diagram that lets users sketch with explicit crossing and twisting assignments. The system extracts feature points from the 2D diagram and uses them as 3D control points. It assigns the heights and over/under relationships of the control points via an optimization and then generates twisted 3D curves using B-splines. An interactive interface links the 2D diagram to the evolving 3D curves, enabling real-time iteration. We validate our method on diverse sketches, compare it with traditional 3D curve construction, and demonstrate its utility for elastic wire art via physics-based animation.Pacific Graphics Conference Papers, Posters, and DemosPosters and Demo