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Interaction in Narrative Medical Visualization: Insights from a Case Study on Tumor Diseases
No full textThis paper examines how basic interaction techniques for medical data, such as clipping planes and manually adjusting the transparency of surfaces, commonly used in professional medical visualizations, can be adapted for medical stories intended for non-expert audiences. We implemented two case studies, involving liver and lung tumors. The goal was to effectively communicate critical aspects such as tumor size, position, and lymph node involvement to our target group. In a mixed-methods online study involving 26 participants, we examined how these techniques facilitate understanding and engagement. Results suggest that transparent surfaces and guided animations, followed by interactive exploration, were beneficial for communicating nested anatomical structures. While promising, these findings are preliminary and not conclusive; further studies with larger samples and real target audiences are needed to validate the effectiveness of these techniques. Our work provides initial design insights for making complex medical content more accessible through interactive storytelling.Eurographics Workshop on Visual Computing for Biology and MedicineSession
RenderMan XPU: A Hybrid CPU+GPU Renderer for Interactive and Final-frame Rendering
No full textRenderMan XPU is a rewrite of Pixar's RenderMan renderer, designed to run on both CPUs and GPUs. Like its predecessor, it is a progressive path tracer, suitable for both interactive previews and high-quality final-frame rendering, but it utilizes modern hardware and software techniques to run significantly faster. Most source code is shared between the two platforms; code for materials (bxdfs) and light transport (integrators) is compiled with a C++ compiler for CPUs and a CUDA compiler for GPUs, with templating, specialization, and a few macros to handle syntax differences and parallel execution abstractions. The shaders that provide the material input values are written in OSL; we use LLVM so that the same OSL code will run on both types of hardware. Only the low-level ray tracing code and texture lookup and caching code is separate. Typical speedups over our previous renderer (for high-quality final-frame images) are 1.8× to 2.3× on CPUs, 5× to 10× on GPUs, and 6× to 15× on both.Computer Graphics ForumGraphics Simulators, Systems and Compilers44
Learning Neural Antiderivatives
No full textNeural fields offer continuous, learnable representations that extend beyond traditional discrete formats in visual computing. We study the problem of learning neural representations of repeated antiderivatives directly from a function, a continuous analogue of summed-area tables. Although widely used in discrete domains, such cumulative schemes rely on grids, which prevents their applicability in continuous neural contexts. We introduce and analyze a range of neural methods for repeated integration, including both adaptations of prior work and novel designs. Our evaluation spans multiple input dimensionalities and integration orders, assessing both reconstruction quality and performance in downstream tasks such as filtering and rendering. These results enable integrating classical cumulative operators into modern neural systems and offer insights into learning tasks involving differential and integral operators.Vision, Modeling, and VisualizationNeural and Differentiable Renderin
Sketching Interactive Experiences: Can Co-creation with Artificial Generative Systems Enhance the Communication of Cultural Heritage?
No full textGenerative AI has opened up new and largely unexplored opportunities to address the challenges of communicating cultural heritage through interactive experiences. This study explores the potential of text-to-image models to support the early stages of interactive media design for cultural heritage applications. We conducted a qualitative survey using four generative AI services (Stable Diffusion, Adobe Firefly, MidJourney, and DALL-E) to address a real design challenge in the cultural domain. While the study does not cover the full scope of traditional interactive media design workflows or provide a comprehensive performance evaluation, it highlights key benefits of generative AI in early design phases. The survey reveals that these systems boost creativity by introducing unexpected elements, they have the ability to help consolidate initial ideas and communicate them effectively to colleagues or stakeholders, and even help less experienced designers understand design requirements.ACM/EG Expressive Symposium - WICED: Eurographics Workshop on Intelligent Cinematography and EditingAI-assisted Co-creatio
Towards Scaling-Invariant Projections for Data Visualization
No full textFinding projections of multidimensional data domains to the 2D screen space is a well-known problem. Multidimensional data often comes with the property that the dimensions are measured in different physical units, which renders the ratio between dimensions, i.e., their scale, arbitrary. The result of common projections, like PCA, t-SNE, or MDS, depends on this ratio, i.e., these projections are variant to scaling. This results in an undesired subjective view of the data, and thus, their projection. Simple solutions like normalization of each dimension are widely used, but do not always give high-quality results. We propose to visually analyze the space of all scalings and to find optimal scalings w.r.t. the quality of the visualization. For this, we evaluate different quality criteria on scatter plots. Given a quality criterion, our approach finds scalings that yield good visualizations with little to no user input using numerical optimization. Simultaneously, our method results in a scaling invariant projection, proposing an objective view to the projected data. We show for several examples that such an optimal scaling can significantly improve the visualization quality.Computer Graphics ForumGeometrically, Parametrically Speaking44
Interactive Stroke-based Neural SDF Sculpting
No full textRecent advances in implicit neural representations have made them a popular choice for modeling 3D geometry. However, directly editing these representations presents challenges due to the complex relationship between model weights and surface geometry, as well as the slow optimization required to update neural fields. Among various editing tools, sculpting stands out as a valuable operation for the graphics and modeling community. While traditional mesh-based tools like ZBrush enable intuitive edits, a comparable high-performance toolkit for sculpting neural SDFs is currently lacking. We introduce a framework that enables interactive surface sculpting directly on neural implicit representations with optimized performance. Unlike previous methods, which are limited to spot edits, our approach allows users to perform stroke-based modifications on the fly, ensuring intuitive shape manipulation without switching representations. By employing tubular neighborhoods to sample strokes and customizable brush profiles, we achieve smooth deformations along user-defined curves, providing intuitive control over the sculpting process. Our method demonstrates that versatile edits can be achieved while preserving the smooth nature of implicit representations, all without compromising interactive performance.High-Performance Graphics - Symposium PapersProcedural Generation and Sculptin
Outside the Wall: Some Applications of TagLab for Semantic Segmentation in Archaeological Practice
No full textImaging techniques have a long history in archeology, from the ruins depicted during the Grand Tour to today's large-scale photogrammetric surveys. Orthophotos generated with photogrammetry are currently extensively used in archeology: a key challenge in this new documentation paradigm lies in the semantic segmentation of such pixel matrices, a process increasingly aided by Machine Learning. This paper focuses on TagLab, an open-source tool originally developed for marine biology that has proven to be extremely versatile in archeological practice. More specifically, this paper aims to expand the use of the software in the Cultural Heritage field and explore its applicability for cost estimation on ancient construction sites, archeological excavations, ceramic thin sections and fragmentary painting documentation.Digital HeritageAnalysing and Documenting Digitized Asset
Prompt Lenses: Improving the Magic of Lenses (for Text Analysis)
No full textIncorporating the analytical power of LLMs with the fast-paced interaction of magic lens-based exploration is an intriguing prospect. Unfortunately, the costs of LLM-generated analyses are high, and applying them continuously seems prohibitive at the moment. Accordingly, we suggest an LLM integration into magic lenses that supports the progressive triggering of costly analyses based on users' interest in the data hovered with the lens. We exemplify this approach with a lens technique for exploring dimensionality-reduced embeddings of visualization paper abstracts shown in a scatterplot. Our proposed approach links back analysis results to the explored visualization improving the comprehensibility and the assessment of the shown results.EuroVis 2025 - Short PapersSystems and Application
Coupling Self-Distillation with Test Time Augmentation for effective LiDAR-Based 3D Semantic Segmentation
No full textEffective 3D perception is fundamental for spatial awareness and safe navigation in modern autonomous systems, with 3D semantic segmentation of LiDAR point clouds being a critical perception task. Recent progress in 2D vision highlights the potential of non-architectural training and inference strategies to further boost model performance. Inspired by consistency-based learning and self-distillation, this work employs such a training pipeline for robust 3D semantic segmentation in street scene understanding. Specifically, we incorporate a teacher-student knowledge self-distillation framework that integrates Test-Time Augmentation to enhance the quality of the soft labels generated by the teacher model during training and to improve inference performance. We present a comparative study on the effectiveness of the employed framework across both convolutional and attention-enhanced networks. Experimental results on the Street3D benchmark dataset demonstrate that the adopted training framework coupled with attention-enhanced networks compares favorably with the state-of-the-art for 3D semantic segmentation in the context of autonomous driving. Code is available at https://github.com/DUTH-VCG/Self_Distillation_with_TTA-mainEurographics Workshop on 3D Object RetrievalShort Paper
Seeing Identity in Data: Can Anthropographics Uncover Racial Homophily in Emotional Responses?
No full textRacial homophily refers to the tendency of individuals to associate with others of the same racial or ethnic background. A recent study found no evidence of racial homophily in responses to mass shooting data visualizations. To increase the likelihood of detecting an effect, we redesigned the experiment by replacing bar charts with anthropographics and expanding the sample size. In a crowdsourced study (N=720), we showed participants a pictograph of mass shooting victims in the United States, with victims from one of three racial groups (Hispanic, Black, or White) highlighted. Each participant was assigned a visualization highlighting either their own racial group or a different racial group, allowing us to assess the influence of racial concordance on changes in affect (emotion). We found that, across all conditions, racial concordance had a modest but significant effect on changes in affect, with participants experiencing greater negative affect change when viewing visualizations highlighting their own race. This study provides initial evidence that racial homophily can emerge in responses to data visualizations, particularly when using anthropographics.EuroVis 2025 - Short PapersEmpirical and Perception Studie